KR20170042613A - Partially synchronized multilateration/trilateration method and system for positional finding using rf - Google Patents

Abstract

Systems and methods for determining the location of one or more user equipments (UEs) in a wireless system may include receiving reference signals via a location management unit having channels located at two or more same locations, The channels located at the same location are strictly synchronized with each other and use the received reference signals to calculate the position of at least one UE among the one or more UEs. Embodiments include multi-channel synchronization with a standard deviation of 10 ns or less. Embodiments may include two LMUs, each LMU having internal synchronization, or one LMU having strictly synchronized signals.

Description

[0001] PARTIALLY SYNCHRONIZED MULTILATERATION / TRILATERATION METHOD AND SYSTEM FOR POSITIONAL FINDING USING RF [0002] FIELD OF THE INVENTION [0003]

Cross reference to related applications

This application claims benefit of U.S. Provisional Patent Application No. 62 / 032,371, filed August 1, 2014, entitled "Partially Synchronized Multivariate Surveying / Trilateration Method and System for Locating Using RF" ; 35 U.S.C. U.S. Provisional Application No. 61 / 514,839, filed on August 3, 2011, entitled Multi-Path Mitigation When Distance-Measuring and Tracking Objects Using Reduced Attenuation RF Technology, under §119 (e) ; U.S. Application No. 61 / 554,945, filed November 2, 2011, entitled Multiple-Path Mitigation When Distance-Measuring and Tracking Objects Using Reduced Attenuation RF Technology; U.S. Application No. 61 / 618,472, filed March 30, 2012, entitled Multiple-Path Mitigation When Distance-Measuring and Tracking Objects Using Reduced Attenuation RF Technology; And U.S. Provisional Application No. 61 / 662,270, filed June 20, 2012, entitled Multiple-Path Mitigation When Distance-Measuring and Tracking Objects Using Reduced Attenuation RF Technology, This application is a continuation-in-part of U.S. Patent Application No. 13 / 566,993, filed August 3, 2012, entitled Multi-Path Mitigation When Distance-Measuring and Tracking Objects Using Attenuated RF Technology, And is incorporated by reference in its entirety.

United States Patent Application Serial No. 13 / 566,993, issued to 35 U.S.C., which is incorporated herein by reference in its entirety. Filed December 15, 2005, entitled Method and System for Reduced Attenuation in Tracking Objects Using Multi-Band RF Technology, under §119 (e) No. 7,561,048, issued July 14, 2009, entitled Methods and Systems for Reduced Attenuation When Tracking Objects Using RF Technology, claiming the benefit of U.S. Patent Application No. 597,649, Published on January 18, 2011, entitled Methods and Systems for Reduced Attenuation in Tracking Objects Using RF Technology, the continuation of U.S. Patent Application Serial No. 11 / 610,595, filed December 14, Partial application of U.S. Patent Application Serial No. 12 / 502,809, filed July 14, 2009, now U.S. Patent No. 7,872,583, entitled Multi-path Mitigation in Tracking Objects Using Reduced Attenuated RF TechnologiesU.S. Patent Application No. 13 / 008,519, filed on June 28, 2011, now U.S. Patent No. 7,969,311 entitled One Methods and System, filed on January 18, 2011, This application is a continuation-in-part of U.S. Patent Application No. 13 / 109,904, filed May 17, 2011, entitled Multiple-Path Mitigation When Distance-Measuring and Tracking Objects Using Damped RF Technology.

U.S. Patent Application Serial No. 12 / 502,809, filed July 14, 2009, entitled Methods and Systems for Reduced Attenuation When Tracking Objects Using RF Technology, Under U.S. 119 (e), United States Provisional Application No. 61 (filed October 7, 2008, entitled Methods and Systems for Multi-Path Mitigation When Tracking Objects Using Reduced Attenuation RF Technology, / 103,270, which is hereby incorporated by reference in its entirety.

Technical field

This embodiment relates to systems for radio frequency (RF) -based identification, tracking and location of objects, including wireless communication and wireless network systems and Real Time Location Services (RTLS) and LTE based location services. will be.

RF-based identification and location-finding systems for determining the relative or geographic location of objects are generally used to track single objects or groups of objects, as well as to track individuals. Conventional position-finding systems have been used for positioning in an open, outdoor environment. RF-based, Global Positioning System (GPS) / Global Navigation Satellite System (GNSS), and Assisted GPSs / GNSS are commonly used. However, conventional location-finding systems suffer certain inaccuracies when locating objects in closed (i.e., indoor) environments as well as outdoors.

Cellular wireless communication systems provide various methods of locating user equipment (UE) locations in indoor and GPS-poor environments. The most accurate methods are positioning techniques based on multivariate / trilateration methods. For example, LTE (Long Term Evolution) Standard Release 9 specifies DL-OTDOA (Downlink Observation Arrival Time Difference) and Release 11 specifies U-TDOA (Uplink Arrival Time Difference), a derivative of multivariate / Identify technologies.

Since time synchronization errors affect location accuracy, the basic requirement for multivariate / trilateration based systems is a perfect and accurate time synchronization of the system to a single common reference time. In cellular networks, DL-OTDOA and U-TDOA localization methods are also used in the case of DL-OTDOA, so that transmissions from multiple antennas are time synchronized, or in the case of U-TDOA, To be synchronized.

The LTE standard Release 9 and Release 11 do not specify time synchronization accuracy for the purpose of location, leaving it to wireless / cellular service providers. On the other hand, these standards provide limitations for distance measurement accuracy. For example, when using a 10 MHz distance measurement signal bandwidth, the requirement is 50 meters at 67% confidence for DL-OTDOA and 100 meters at 67% confidence for U-TDOA.

These known limitations are the result of distance measurement errors and combinations of errors caused by lack of precise synchronization, e.g. time synchronization errors. From the relevant LTE test specifications (3GPP TS 36.133 version 10.1.0 release 10) and other documents, it is possible to estimate time synchronization errors, assuming that synchronization errors are uniformly distributed. One such estimate reaches 200 ns (100 ns peak-to-peak). It should be noted that the voice over LTE (VoLTE) function also requires cellular network synchronization down to 150 nanoseconds (75 ns peak-to-peak), assuming that synchronization errors are distributed evenly. Therefore, in the future, the time synchronization accuracy of the LTE network will be assumed to be within 150 ns.

For distance location accuracy, the FCC Directive NG 911 specifies 50-meter and 100-meter location accuracy requirements. However, for the location-based service (LBS) market, indoor location requirements are much more stringent - 3 meters at 67% confidence. Thus, the distance measurement and positioning error introduced by the time synchronization error of 150 ns (standard deviation of 43 ns) is much greater than the 3 meter position measurement error (standard deviation of 10 ns).

Although time synchronization of the cellular network may be appropriate to meet the mandatory FCC NG E911 emergency location requirements, this synchronization accuracy does not meet the needs of LBS or RTLS system users, which require significantly more precise location. Thus, there is a need in the art for mitigating location errors induced by the lack of accurate time synchronization for cellular / wireless networks for purposes of supporting LBS and RTLS.

This disclosure relates to methods for radio frequency (RF) -based identification, tracking and positioning of objects, including real-time location services (RTLS) systems that substantially eliminate one or more of the disadvantages associated with existing systems, Systems. The methods and systems may use partially synchronized (in time) receivers and / or transmitters. According to an embodiment, RF-based tracking and location is implemented in cellular networks, but may also be implemented in any wireless system and RTLS environments. The proposed system can use software implemented digital signal processing and software defined radio techniques (SDR). Digital signal processing (DSP) can also be used.

One approach described herein uses precisely time-synchronized receivers and / or clusters of transmitters in each cluster, but inter-cluster time synchronization is much less accurate or not required at all. This embodiment may be used in Mopen wireless systems / networks and may include unidirectional, half-duplex and full-duplex modes of operation. The embodiments described below operate in conjunction with wireless networks that use various modulation types, including OFDM modulation and / or its derivatives. Thus, the embodiment described below operates in conjunction with LTE networks and it is also applicable to other wireless systems / networks.

As described in one embodiment, RF-based tracking and locating implemented on 3GPP LTE cellular networks will benefit significantly from precisely synchronized (in time) receivers and / or clusters of transmitters. The proposed system may use software- and / or hardware-implemented digital signal processing.

Additional features and advantages of the embodiments will be set forth in the description that follows, and in part will be obvious from the description, or may be learned by practice of the embodiments. The advantages of the embodiments will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

It is to be understood that both the foregoing general description and the following detailed description are exemplary, explanatory and are intended to provide further explanation of the embodiments, as claimed.

The accompanying drawings, which are included to provide a further understanding of the embodiments and which are incorporated in and constitute a part of this specification, illustrate embodiments and serve to explain the principles of the embodiments together with the description.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 and FIG. 1A illustrate narrow bandwidth distance measurement signal frequency components, according to an embodiment;
Figure 2 illustrates representative wide bandwidth distance measurement signal frequency components;
Figures 3A, 3B, and 3C illustrate block diagrams of master and slave units of an RF mobile tracking and location system, in accordance with an embodiment;
4 illustrates an embodiment of a synthesized wideband baseband distance measurement signal;
5 illustrates removal of a signal precursor by erasure, according to an embodiment;
6 illustrates precursor erasure with fewer carriers, according to an embodiment;
Figure 7 illustrates an embodiment of a step-and-step transfer function step;
Figure 8 illustrates an embodiment of a method of locating;
Figure 9 illustrates LTE reference signal mapping;
10 illustrates an embodiment of an enhanced cell ID + RTT locating technique;
Figure 11 illustrates an embodiment of an OTDOA locating technique;
12 illustrates operation of a time observation unit (TMO) installed in an operator's eNB facility, according to an embodiment;
Figure 13 illustrates an embodiment of a wireless network location equipment diagram;
14 illustrates an embodiment of a wireless network location downlink ecosystem for enterprise applications;
15 illustrates an embodiment of a wireless network location downlink ecosystem for network wide applications;
Figure 16 illustrates an embodiment of a wireless network location uplink ecosystem for enterprise applications;
Figure 17 illustrates an embodiment of a wireless network location uplink ecosystem for network wide applications;
Figure 18 illustrates an embodiment of a UL-TDOA environment that may include one or more DAS and / or femto / small cell antennas;
Figure 19 illustrates an embodiment of a UL-TDOA as in Figure 18 that may include one or more cell towers that may be used in place of DAS base stations and / or femto / small cells;
Figure 20 illustrates an embodiment of cell level location;
Figure 21 illustrates an embodiment of serving cell and sector ID location;
Figure 22 illustrates an embodiment of E-CID plus AoA positioning;
Figure 23 illustrates an embodiment of AoA positioning;
24 illustrates an embodiment of a TDOA with wide and close distances between receive antennas;
Figure 25 illustrates an embodiment of a three sector arrangement;
26 illustrates an embodiment of antenna port mapping;
Figure 27 illustrates an embodiment of LTE Release 11 U-TDOA locating technology;
28 illustrates an embodiment of a multi-channel location management unit (LMU) high-level block diagram;
29 illustrates an embodiment of a DL-OTDOA technique in a wireless / cellular network with a location server;
30 illustrates an embodiment of a U-TDOA technique in a wireless / cellular network with a location server;
Figure 31 illustrates an embodiment of a depiction of a rack mount enclosure;
32 illustrates an embodiment of a high-level block diagram of a number of single channel LMUs clustered (integrated) in a rackmount enclosure;
33 illustrates an embodiment of a high-level block diagram of multiple small cells with an integrated LMU clustered (integrated) in a rackmount enclosure (one-to-one antenna connection / mapping);
34 illustrates an embodiment of a high level block diagram of LMUs and DAS integration.
Figure 35 illustrates an embodiment of a high level block diagram of LMUs and WiFi infrastructure integration.

Reference will now be made in detail to preferred embodiments of the present embodiments, examples of which are illustrated in the accompanying drawings.

The embodiments are directed to methods and systems for RF-based identification, tracking, and locating of objects, including RTLS. According to an embodiment, the method and system use a narrow bandwidth distance measurement signal. The embodiment operates in the VHF band, but can also be used in the HF, LF, and VLF bands as well as in the UHF band and above. It uses a multi-path mitigation processor. Using a multi-path mitigation processor increases the accuracy of tracking and locating implemented by the system.

Embodiments include small, highly portable base units that allow users to track, locate and monitor a large number of people and objects. Each unit has its own ID. Each unit broadcasts an RF signal with its ID, and each unit can send back a return signal, which may include its ID as well as voice, data, and side information. Each unit processes the returned signals from other units and continues to determine their relative and / or actual positions, depending on triangulation or trilateration and / or other methods used. The preferred embodiment can also be easily integrated with products such as GPS devices, smart phones, bi-directional radios and PDAs. The resulting product will have all of the functionality of stand-alone devices, leveraging the processing capacity of existing displays, sensors (such as altimeter, GPS, accelerometers and compasses) and its host. For example, a GPS device with the device technology described herein would be able to provide the location of the user on the map as well as to map the locations of other members of the group.

The size of the preferred embodiment based on FPGA implementation is between about 2x4x1 inches and 2x2x0.5 inches or less as the integrated circuit technology is improved. Depending on the frequency used, the antenna may be integrated into the device or project through the device enclosure. The device's application specific integrated circuit (ASIC) -based version will be able to integrate most of the functionality of the FPGA and other electronic components into units or tags. The ASIC-based standalone version of the product will result in a device size of less than 1 x 0.5 x 0.5 inches. The antenna size will be determined by the frequency used and a portion of the antenna may be integrated into the enclosure. An ASIC-based embodiment designed to be integrated into products can be made only by chipset. There should be no substantial physical size difference between the Master or Tag units.

Devices can use standard system components (custom component components) that operate in multiple frequency ranges (bands) for the processing of multi-path mitigation algorithms. Software for digital signal processing and software-defined radios can be used. Signal processing software combined with minimal hardware allows to assemble radios with transmitted and received waveforms defined by software.

U.S. Patent No. 7,561,048 discloses a narrow-bandwidth distance measurement signal system whereby a narrow-bandwidth distance measurement signal can be obtained, for example, only in a few kilohertz widths (some of the low-bandwidth channels are extended to tens of kilohertz Bandwidth channels, but using voice channels that are low-bandwidth channels (although possible). This contrasts with conventional position-finding systems that use channels from hundreds of kilohertz to tens of megahertz wide.

The advantages of such a narrow-bandwidth distance measuring signal system are as follows: 1) At lower operating frequencies / bands, conventional position-finding systems distance measuring signal bandwidth exceeds the carrier (operating) frequency value. Thus, such systems, including HF, can not be placed in the LF / VLF and other lower frequency bands. Unlike conventional position-finding systems, the narrow-bandwidth distance measuring signaling system described in U.S. Patent No. 7,561,048 is not suitable for LF, VLF, and other bands because its distance measurement signal bandwidth is well below the carrier frequency value Lt; / RTI > 2) In the lower stages of the RF spectrum (in some VLF, LF, HF and VHF bands), up to the UHF band, for example, conventional position-finding systems require the FCC to make use of conventional distance- It can not be used because it strictly limits the allowable channel bandwidth (12 kHz to 25 kHz). Unlike conventional position-finding systems, the distance measurement signal bandwidth of the narrow-bandwidth distance measurement signal system is entirely in line with FCC regulations and other international spectrum regulatory agencies; And 3) independent of the operating frequency / band, it is well known that narrow-bandwidth signals have inherently higher SNR (signal-to-noise-ratio) compared to light-bandwidth signals (Ray H. Hashemi , By William G. Bradley, MRI: BASICS - 2003). This increases the operating range of the narrow-bandwidth ranging signal location-finding system independently of the frequency / band in which it operates, including the UHF band.

Thus, unlike conventional position-finding systems, the narrow-bandwidth ranging signal location-finding system is located below the LF-VLF bands and below the lower end of the RF spectrum, e.g., below the VHF frequencies Where multipath phenomena are less noticeable. At the same time, the narrow-bandwidth distance measurement position-finding system can also be deployed in the UHF band or above, thereby improving the distance measurement signal SNR and, consequently, the position-finding system operating range.

It is desirable to operate on the VLF / LF bands to minimize multipath, e.g., RF energy reflections. However, at these frequencies, the efficiency of the portable / mobile antenna is very small (less than about 0.1% due to the small antenna length (size) for the RF wavelength). Also, at these lower frequencies, the noise level from natural and artificial sources is much higher than at higher frequencies / bands, e.g., VHF. Together, these two phenomena can limit the applicability of the position-finding system, for example its operating range and / or portability / portability. Therefore, for certain applications where operating range and / or mobility / portability are very important, higher RF frequencies / bands such as HF, VHF, UHF and UWB may be used.

In the VHF and UHF bands, the noise level from natural and artificial sources is significantly lower compared to the VLF, LF and HF bands; At VHF and HF frequencies, multi-path phenomena (e.g., RF energy reflections) are less severe than at UHF frequencies. Also, at VHF, antenna efficiency is significantly better than at HF and lower frequencies, and RF penetration capabilities at VHF are much better than at UHF. Thus, the VHF band provides a good compromise for mobile / portable applications. On the other hand, in some special cases, such as GPS where VHF frequencies (or lower frequencies) can not penetrate the ionosphere (or are deflected / refracted), UHF may be a good choice. However, in any case (and in all cases / applications), the narrow-bandwidth distance measurement signaling system will have advantages over conventional optical-bandwidth distance measuring signal location-finding systems.

The actual application (s) will determine the correct technical specifications (such as power, emissions, bandwidth, and operating frequencies / bandwidth). Narrow-bandwidth-distance measurements may be used to determine whether a user receives licenses or receives an exemption from licenses, or that the narrow-band distance measurement is the most stringent narrow bandwidths specified in the FCC: 6.25 kHz, 11.25 kHz, 12.5 kHz, 25 kHz and 50 kHz , Use unqualified bands as set forth in the FCC and allow to comply with corresponding technical requirements for the appropriate sections, since it allows operation on many different bandwidths / frequencies. As a result, multiple FCC sections and exemptions within these sections will be applicable. The main FCC rules applicable are: 47 CFR Part 90 - Private Land Mobile Radio Services, 47 CFR Part 94 Personal Radio Services, 47 CFR Part 15 - Radio Frequency Devices. (By comparison, the broadband signal in this context is from 10 to 20 MHz at several hundred KHz).

Typically, for parts 90 and 94, VHF implementations allow the user to operate the device up to 100 mW under certain exemptions (low power radio service is an example). For certain applications, the allowable transmit power in the VHF band is between two watts and five watts. For 900 MHz (UHF band), it is 1W. On the 160 kHz to 190 kHz frequencies (LF band), the allowable transmit power is 1 watt.

Narrowband distance measurements can follow many, but not all, of the different spectral tolerances and allow accurate distance measurements while still meeting the most stringent regulatory requirements. This is valid only for the FCC, but for other international organizations regulating the use of the spectrum worldwide, including Europe, Japan and Korea.

The following is a list of common frequencies used with typical power usage and distance, and the tag can communicate with another reader in a real-world environment (Indoor Propagation and Wavelengths Dan Dobkin, WJ Communications, V 1.4.7 / 10 / 02):

915 MHz 100 mW 150 feet

2.4 GHz 100 mW 100 feet

5.6 Ghz 100 mW  75 feet

The proposed system operates at VHF frequencies and uses proprietary methods for transmitting and processing RF signals. More specifically, it uses DSP technologies and software-defined radio (SDR) to overcome the limitations of narrow bandwidth requirements at VHF frequencies.

Operating at lower (VHF) frequencies reduces scattering and provides much better wall penetration. The net result is an approximately 10-fold increase in frequency over commonly used frequencies. For example, let's compare the measured ranges of prototypes to those listed above:

216 MHz 100 mW 700 feet

Using narrowband distance measurement techniques, the tag communication range will have the usual power usage and distance to be able to communicate with another reader in a real world environment, and the range of commonly used frequencies will increase significantly:

from: Till:

915 MHz 100 mW 150 feet 500 feet

2.4 GHz 100 mW 100 feet 450 feet

5.6 Ghz 100 mW 75 feet 400 feet

Battery consumption is a function of the design of the device, the transmit power and the duty cycle, for example the time interval between two successive distance (position) measurements. In many applications, the duty cycle is large, from 10X to 1000X. In applications with large duty cycles, for example, 100X, an FPGA version that transmits 100 mW of power will have approximately three weeks of uptime. The ASIC-based version is expected to increase uptime by 10X. In addition, ASICs have inherently lower noise levels. Thus, the ASIC-based version can also increase the operating range by about 40%.

It will be appreciated by those of ordinary skill in the art that the system does not compromise the long operating range while significantly increasing location-finding accuracy in RF challenging environments (e.g., buildings, urban corridors, etc.).

Typically, tracking and location systems use tracking-location-navigation methods. These methods include a combination of arrival time (TOA), arrival time difference (DTOA) and TOA and DTOA. As a distance measurement technique, the time of arrival (TOA) is generally described in U.S. Patent No. 5,525,967. The TOA / DTOA-based system measures the RF distance measurement signal direct-line-of-site (DLOS) flight time, for example time delay, which is then converted to a distance range.

In the case of RF reflections (e.g., multi-path), multiple copies of the RF distance measurement signal with various delay times are superimposed on the DLOS RF distance measurement signal. Tracking-locating systems using narrow bandwidth distance measurement signals can not distinguish between DLOS signals and reflected signals without multi-path mitigation. As a result, these reflected signals cause errors in the estimated distance measurement signal DLOS flight time, which in turn affects the distance estimation accuracy.

The embodiment advantageously uses a multi-path mitigation processor to separate the DLOS signal and the reflected signals. Thus, the embodiment significantly reduces the error in the estimated distance measurement signal DLOS flight time. The proposed multi-path mitigation method can be used on all RF bands. It can also be used with wide bandwidth distance measurement signal position-finding systems. It can support a variety of modulation / demodulation techniques, including spread spectrum techniques such as DSS (Direct Spread Spectrum) and FH (Frequency Hopping).

Additionally, noise reduction methods can be used to further improve the accuracy of the method. These noise reduction methods may include, but are not limited to, coherent summation, non-coherent summation, matched filtering, time diversity techniques, and the like. The remainder of the multi-path interference error may be further reduced by using post-processing techniques such as maximum likelihood estimation (e.g., Viterbi algorithm), minimum variance estimation (Kalman filter), and the like.

Embodiments may be used in systems having unidirectional, half-duplex and full-duplex modes of operation. Full-duplex operation is very costly for complexity, cost and logistics on RF transceivers, which limits the range of system operation in portable / mobile device implementations. In a half-duplex mode of operation, a reader (sometimes referred to as a " master ") and tags (sometimes also referred to as" slaves "or" targets ") merely allow a master or slave to transmit at any given time Lt; / RTI >

An alternative to transmission and reception allows a single frequency to be used for distance measurements. This arrangement reduces the cost and complexity of the system compared to full duplex systems. The unidirectional mode of operation is conceptually simpler, but involves a more thorough synchronization of events between the master and target unit (s), including a distance measurement signal sequence.

In these embodiments, the narrow bandwidth distance measurement signal multipath mitigation processor does not increase the distance measurement signal bandwidth. It advantageously uses different frequency components to allow propagation of the narrow bandwidth ranging signal. The additional distance measurement signal processing assembles a composite distance measurement positive signal in the frequency domain or with a relatively large bandwidth by using statistical algorithms such as super resolution spectral estimation algorithms (MUSIC, rootMUSIC, ESPRIT) and / or RELAX, Lt; RTI ID = 0.0 > time-domain. ≪ / RTI > The different frequency components of the narrow bandwidth ranging measurement signal may be pseudo-randomly selected, which may also be adjacent or spaced in frequency, which may have uniform and / or non-uniform spacing.

Embodiments extend multipath mitigation techniques. A signal model for narrowband range measurement is defined as a complex index whose frequency is directly proportional to the delay defined by the range (as introduced elsewhere in this document) plus a similar Condition. The model is independent of the actual implementation of the signal structure, e.g., step frequency, linear frequency modulation, and the like.

The frequency spacing between the direct path and the multipath is nominally small and normal frequency domain processing is not sufficient to estimate the direct path range. For example, a step frequency distance measurement signal (100.07 nanosecond delay) at a 100 KHz step rate ranging from 30 meters to 5 MHz leads to a frequency of 0.062875 radians / second. Multipath reflections with a path length of 35 meters will cause a frequency of 0.073355. The interval is 0.0104792. The frequency resolution of the 50 observable samples has a native frequency resolution of 0.12566 Hz. As a result, it is not possible to accurately estimate the direct path range using conventional frequency estimation techniques for direct path separation from the reflected path.

To overcome these limitations, embodiments use a unique combination of implementations of subspace decomposition high resolution spectral estimation methods and multimode cluster analysis. The subspace decomposition technique relies on decomposing the estimated covariance matrix of the observed data into two orthogonal subspaces, a noise subspace and a signal subspace. The theory of the subspace decomposition method is that the projection of the object to the noise subspace is done by noise only and the projection of the object to the signal subspace is made up of signals only.

The super resolution spectral estimation algorithms and the RELAX algorithm can identify nearby frequencies (sinusoids) in the spectrum in the presence of noise. The frequencies need not be related to harmonics, and unlike the digital Fourier transform (DFT), the signal model does not introduce any artificial periodicity. For a given bandwidth, these algorithms provide significantly higher resolution than Fourier transforms. Thus, direct high line (DLOS) can be reliably distinguished from other multi-paths (MP) with high accuracy. Similarly, applying a threshold value method, which will be described later, to a wider bandwidth distance measurement signal of artificially generated synthesis makes it possible to reliably distinguish DLOS from other paths with high accuracy.

According to an embodiment, digital signal processing (DSP) can be used by a multi-path mitigation processor to reliably distinguish DLOS from other MP paths. Various super-resolution algorithms / techniques exist in the spectrum analysis (spectral estimation) technique. Examples include subspace-based methods: MUSIC (MULTIPLE SIIGNAL CHARACTERIZATION) algorithm or root-MUSIC algorithm, Estimation of signal parameters through rotation invariance techniques (ESPRIT) algorithm, Pisarenko harmonic decomposition (PHD) algorithm, RELAX algorithm and so on.

Known super-resolution algorithms operate on the premise that signals that impinge on the antenna are not completely correlated. Thus, performance can be confronted in multipath propagation and is severely degraded in highly correlated signal environments. Multipath mitigation techniques can involve preprocessing techniques called spatial smoothing. As a result, the multipath mitigation process can be computationally intensive and complicated, which can increase the complexity of the system implementation. Multipath mitigation with lower system computational costs and implementation complexity can be achieved by using a super-resolution Matrix Pencil (MP) algorithm. The MP algorithm is classified as a non-search procedure. It is therefore computationally less complex and eliminates the problems encountered in the search procedures used in other super-resolution algorithms. In addition, the MP algorithm is not sensitive to correlated signals and can only estimate single channel estimates and delays associated with coherent multipath components.

In both of the above-mentioned super-resolution algorithms, the incoming (i.e., received) signal is modeled as a linear combination of complex exponents of frequencies and their complex amplitudes. In the case of a multi-path, the received signal would be:

, (1)

, (One)

는 송신된 신호이고, f는 동작 주파수이고, L은 다중-경로 구성요소들의 수이며,

및 τ_K는 각각 제K 경로의 복소 감쇠 및 전파 지연이다. 다중-경로 구성요소들은 전파 지연들이 오름차순으로 고려되도록 인덱싱된다. 그 결과, 이 모델에서, τ₀은 DLOS 경로의 전파 지연을 나타낸다. 명확하게, τ₀ 값은, 그것이 모든 τ_K의 최소 값이기 때문에, 가장 관심 있다. 상(θ_K)은 명목상 균일 확률 밀도 함수(U(0,2π))를 갖고 하나의 측정 사이클에서 또 다른 것으로 랜덤하게 추정된다. 따라서, 우리는 α_K=const(즉, 상수 값)이라고 가정한다. From here

Is the transmitted signal, f is the operating frequency, L is the number of multipath components,

And? _K are the complex attenuation and propagation delay of the Kth path, respectively. The multipath components are indexed such that the propagation delays are considered in ascending order. As a result, in this model,? ₀ represents the propagation delay of the DLOS path. Clearly, the value of τ ₀ is of greatest interest, since it is the minimum value of all τ _K. The phase < RTI ID = 0.0 > ( _k ) < / RTI > is nominally a random probability density function (U (0,2π)) and is randomly estimated in one measurement cycle as another. Thus, we assume that α _K = const (ie, a constant value).

The parameters [alpha] _K and [kappa] _K are random time-varying functions that reflect the motions of people and equipment in and around buildings. However, since the rate of their deformation is very slow compared to the measurement time interval, these parameters can be processed as time-invariant random variables within a given measurement cycle.

All of these parameters are frequency-dependent as they relate to radio signal characteristics, such as transmission and reflection coefficients. However, in an embodiment, the operating frequency varies very slightly. Thus, the above-mentioned parameters can be assumed to be frequency-independent.

Equation (1) can be shown in the frequency domain as:

, (2)

, (2)

(2? X? _K ) is the artificial "frequencies" to be estimated by the super-resolution algorithm and the operating frequency f is an independent variable; A (f) is the complex amplitude of the received signal; α _K is the K-th path amplitude.

In equation (2), the (2π × τ _K ) and then the super-resolution estimate of τ _K values is based on continuous frequency. In practice, there are a finite number of measurements. Therefore, the variable f is not a continuous variable but rather a discrete variable. Therefore, the complex amplitude A (f) can be calculated as follows:

, (3)

, (3)

은 이산 주파수들(f_n)에서 이산 복소 진폭 추정치들(즉, 측정치들)이다. From here

Is discrete complex amplitude estimates (i.e., measurements) at discrete frequencies f _n .

은 그것이 다중-경로 채널을 통해 전파한 후 주파수(f_n)의 정현파 신호의 진폭 및 위상으로서 해석될 수 있다. 모든 스펙트럼 추정 기반 초-분해능 알고리즘들은 복소 입력 데이터(즉, 복소 진폭)를 요구한다는 것을 주의하자.In equation (3)

Can be interpreted as the amplitude and phase of the sinusoidal signal of frequency f _n after it propagates through the multi-path channel. Note that all spectral estimation based super-resolution algorithms require complex input data (i.e., complex amplitude).

를 복소 신호(예로서, 분석 신호)로 변환하는 것이 가능하다. 예를 들면, 이러한 변환은 힐버트(Hilbert) 변환 또는 다른 방법들을 사용함으로써 실현될 수 있다. 그러나, 짧은 거리들의 경우에, 값(τ₀)은 매우 작으며, 이것은 매우 낮은 (2π×τ_K) "주파수들"을 야기한다. In some cases, actual signal data, e.g.,

(E.g., an analysis signal). For example, this transformation can be realized by using Hilbert transform or other methods. However, in the case of short distances, the value (? ₀ ) is very small, which results in very low (2? X? _K ) "frequencies".

)만이 사용된다면, 추정될 주파수들의 수는 (2π×τ_K) "주파수들"뿐만 아니라, 그것들의 조합들을 또한 포함할 것이다. 일반적으로, 알려지지 않은 주파수들의 수를 증가시키는 것은 초-분해능 알고리즘들의 정확도에 영향을 준다. 따라서, 다른 다중-경로(MP) 경로들로부터 DLOS 경로의 신뢰성 있는 정확한 분리는 복소 진폭 추정을 요구한다.These low "frequencies" create problems with Hilbert transform (or other methods) implementations. Also, only amplitude values (e.g.,

) Is used, the number of frequencies to be estimated will also include (2 pi x _K ) "frequencies" as well as combinations thereof. In general, increasing the number of unknown frequencies affects the accuracy of the super-resolution algorithms. Thus, reliable and accurate separation of the DLOS path from other multi-path (MP) paths requires complex amplitude estimation.

)을 획득하는 태스크 동안 방법 및 다중-경로 완화 프로세서 동작에 대한 설명이다. 설명이 동작의 반-이중 모드에 초점이 맞춰지지만, 그것은 전-이중 모드에 대해 쉽게 확장될 수 있다는 것을 주의하자. 동작의 단방향 모드는 반-이중 모드의 서브세트이지만, 부가적인 이벤트 동기화를 요구할 것이다. The following shows the complex amplitude (

) And a description of the multi-path mitigation processor operation. Note that while the focus is on the half-duplex mode of this operation, it should be noted that it can be easily extended for full-duplex mode. The unidirectional mode of operation is a subset of the half-duplex mode, but will require additional event synchronization.

In a half-duplex mode of operation, a reader (sometimes referred to as a " master ") and tags (also referred to as" slaves "or" targets ") merely require a master or slave to transmit at any given time . In this mode of operation, the tags (target devices) act as transponders. The tags receive the distance measurement signal from the reader (master device), store it in memory and then re-transmit the signal back to the master after a certain time (delay).

Examples of distance measurement signals are shown in Figs. 1 and 1A. Representative distance measurement signals use adjacent frequency components. Other waveforms, including pseudo-random, frequency and / or temporally spaced or orthogonal, may also be used as long as the distance measurement signal bandwidth remains narrow. In Figure 1, the duration (T _f ) for all frequency components is long enough to obtain the distance measurement signal narrow-bandwidth property.

Another variation of the distance measurement signal with different frequency components is shown in Fig. It comprises a plurality of frequencies (f ₁ , f ₂ , f ₃ , f ₄ , f _n ) transmitted over a long period of time to make the individual frequencies narrow-band. These signals are more efficient, but they take up bandwidth and the bandwidth measurement signals affect the SNR, which in turn reduces the operating range. In addition, such a wide bandwidth distance measurement signal would violate the FCC requirements for frequencies below the VHF band. However, in certain applications, this light-bandwidth distance measurement signal allows for easier integration into existing signaling and transmission protocols. In addition, these signals reduce tracking-locating time.

The multi-frequency _{(f 1, f 2, f} 3, f 4, f n) bursts are also adjacent and / or pseudo-random, and may be spaced or orthogonal in the frequency and / or time.

Narrowband distance measurement mode will produce accuracy in the form of a momentary wideband distance measurement, increasing the range at which such an accuracy can be realized, as compared to a wideband distance measurement. This performance is achieved at a fixed transmit power, since the SNR (at the appropriate signal bandwidths) at the receiver of the narrowband distance measurement signal is greater than the SNR at the receiver of the wideband distance measurement signal. The SNR gain is approximately the ratio of the total bandwidth of the wideband distance measurement signal and the bandwidth of each channel of the narrowband distance measurement signal. This provides a good trade-off when very fast distance measurements are not required for, for example, moving and slow moving targets, such as walking or running people.

The master devices and the tag devices are identical and can operate in master or transponder mode. All devices include data / remote control communication channels. The devices can exchange information and the master device (s) can remotely control the tag devices. In this example depicted in Figure 1, during operation of the master (i.e., reader), the multi-path mitigation processor generates a distance measurement signal for the tag (s) Lt; / RTI >

추정치들을 결정한다. 식(3)에서,

은 단-방향 거리 측정 신호 트립에 대해 정의된다는 것을 주의하자. 실시예에서, 거리 측정 신호는 왕복을 한다. 다시 말해서, 그것은 양쪽 방향들로 이동한다: 마스터/판독기로부터 타겟/슬레이브로 및 타겟/슬레이브로부터 다시 마스터/판독기로. 따라서, 마스터에 의해 다시 수신되는 이러한 왕복 신호 복소 진폭은 다음과 같이 산출될 수 있다:The master's multi-path attenuation processor then compares the received distance measurement signal to that transmitted from the original master and generates a signal in the form of amplitude and phase for all frequency components f _n

Estimates are determined. In equation (3)

Is defined for the short-direction distance measurement signal trip. In an embodiment, the distance measurement signal makes a round trip. In other words, it moves in both directions: from the master / reader to the target / slave and again from the target / slave to the master / reader. Thus, the reciprocal signal complex amplitude received again by the master can be calculated as follows:

(4)

(4)

및

)을 포함하여, 복소 진폭 및 위상 값들을 추정하기 위해 이용 가능한 많은 기술들이 있다. 실시예에 따르면, 복소 진폭 결정은 마스터 및/또는 태그 수신기 RSSI(수신 신호 세기 표시자) 값들로부터 도출된

값들에 기초한다. 상기 위상 값들(

)은 판독기/마스터 리턴 기저대역 거리 측정 신호 위상에 의해 수신된 것 및 원래 (즉, 판독기/마스터에 의해 전송된) 기저대역 거리 측정 신호 위상을 비교함으로써 획득된다. 또한, 마스터 및 태그 디바이스들이 독립적인 클록 시스템들을 갖기 때문에, 디바이스 동작에 대한 상세한 설명은 위상 추정 에러에 대한 클록 정확도 영향의 분석에 의해 증강된다. 상기 설명이 도시하는 바와 같이, 단-방향 진폭(

) 값들은 타겟/슬레이브 디바이스로부터 직접 획득 가능하다. 그러나, 단-방향 위상(

) 값들은 직접 측정될 수 없다. For example, matching filtering (

And

), There are many techniques available for estimating complex amplitude and phase values. According to an embodiment, the complex amplitude determination may be performed on the basis of the master and / or tag receiver RSSI (Received Signal Strength Indicator) values

Lt; / RTI > The phase values (

Is obtained by comparing the phase of the signal received by the reader / master return baseband distance measurement signal phase and the phase of the baseband distance measurement signal originally transmitted (i.e., transmitted by the reader / master). In addition, since the master and tag devices have independent clock systems, a detailed description of device operation is augmented by analysis of the clock accuracy impact on phase estimation error. As described above, the short-side amplitude (

) Values are obtainable directly from the target / slave device. However, the short-direction phase (

) Values can not be measured directly.

In an embodiment, the distance measurement baseband signal is the same as depicted in Fig. However, for the sake of simplicity, it is assumed herein that the distance-measuring baseband signal consists of only two frequency components, each comprising a plurality of periods of cosine or sine waves of different frequencies: F ₁ and F ₂ . Note that F _{1 =} f ₁ and F _{2 =} f ₂ . The number of periods in the first frequency component is L and the number of periods in the second frequency component is P. Note that for a T _{f =} constant, L may be equal to or not equal to P, since each frequency component may have a different number of periods. Also, there is no time gap between each frequency component, and both F ₁ and F ₂ start from an initial phase, such as zero.

Figures 3a, 3b and 3c depict block diagrams of master or slave units (tags) of an RF mobile tracking and location system. F _OSC represents the frequency of the device system clock (crystal oscillator 20 in FIG. 3A). All frequencies generated within the device originate from such a system clock crystal oscillator. The following definitions are used: M is the master device (unit); AM is a tag (target) device (unit). The tag device operates in transponder mode and is referred to as a transponder (AM) unit.

.In a preferred embodiment, the device comprises an RF front-end and an RF back-end, a base-band and a multi-path mitigation processor. An RF back-end, base-band, and multi-path mitigation processor is implemented in FPGA 150 (see Figures 3B and 3C). System clock generator 20 (see FIG. 3A) has F _{OSC =} 20 MHz; Or oscillation at omega _{OSC =} 2 pi x 20 x 10 ⁶ . This is an ideal frequency because, in real devices, the system clock frequencies are not always the same as 20 MHz:

.

임을 주의하자.

.

It should be noted that F _OSC frequencies other than 20 MHz can be used without any effect on system performance.

The electronic configuration of both units (master and tag) is the same and the different modes of operation are software programmable. The baseband distance measurement signal is generated in digital form by the master's FPGA 150, blocks 155-180 (see FIG. 2B). It consists of two frequency components, each of which contains multiple periods of cosine or sine waves of different frequencies. At the first (t = 0), the FPGA 150 in the master device (FIG. 3B) routes the digital base-band distance measurement signal to its up-converter 50 via I / Q DACs 120 and 125 Output. The FPGA 150 starts at an F ₁ frequency and begins to generate an F ₂ frequency for a duration of T ₂ after a time T ₁ .

Since the frequency of the crystal oscillator may be different from 20 MHz, the actual frequencies generated by the FPGA will be F ₁ γ ^M and F ₂ γ ^M. Also, the time (T ₁ ) will be T ₁ β ^M and T ₂ will be T ₂ β ^M. T ₁ , T ₂ , F ₁ , F ₂ are F ₁ γ ^M T ₁ ? ^{M =} F ₁ T ₁ and F ₂ ? ^M * T ₂ ? ^{M =} F ₂ T ₂ , where F ₁ T ₁ & F ₂ T ₂ are both integers. It means that the initial phases of F ₁ and F ₂ are equal to zero.

Since all frequencies are generated from the system crystal oscillator 20 clocks, the outputs of the master's baseband I / Q DAC (s) 120 and 125 are as follows:

및

, 여기에서

및

는 상수 계수들이다. 유사하게, 주파수 합성기(25)(믹서들(50 및 85)을 위한 LO 신호들)로부터의 출력 주파수들(TX_LO 및 RX_LO)은 상수 계수들을 통해 표현될 수 있다. 이들 상수 계수들은 마스터(M) 및 트랜스폰더(AM)에 대해 동일하며 - 차이는 각각의 디바이스의 시스템 수정 발진기(20) 클록 주파수에 있다.

And

, From here

And

Are constant coefficients. Similarly, the output frequencies TX_LO and RX_LO from the frequency synthesizer 25 (LO signals for mixers 50 and 85) may be represented by constant coefficients. These constant coefficients are the same for the master (M) and the transponder (AM) - the difference is at the system crystal oscillator (20) clock frequency of each device.

The master (M) and the transponder (AM) operate in a half-duplex mode. The master RF front-end uses the quadrature up-converter (i.e., mixer) 50 to up-convert the base-band distance measurement signal generated by the multi-path mitigation processor, . After the base-band signal is transmitted, the master switches from TX to RX mode using the RF front-end TX / RX switch 15. The transponder receives the received signal using its RF front-end mixer 85 (which generates the first IF) and the ADC 140 (which generates the second IF) and down-converts it again .

This second IF signal is then digitally filtered at the transponder RF back-end using the digital filters 190 and output to the RF back-end quadrature mixer 200, the digital I / Q filters 210 and 230, The digital orthogonal oscillator 220 and the summer 270 to further down-convert to a base-band distance measurement signal. This base-band distance measurement signal is stored in the memory 170 of the transponder using the Ram data bus controller 195 and the control logic 180. [

. 마스터는 트랜스폰더 송신을 수신하며 그것의 RF 백-엔드 직교 믹서(200), 디지털 I 및 Q 필터들(210 및 230), 디지털 직교 발진기(220)를 사용하여 수신된 신호를 다시 기저-대역 신호로 하향-변환한다(도 3c 참조). The transponder then switches from RX to TX mode using the RF front-end switch 15 and, after a specified delay (t _RTX ), begins to re-transmit the stored baseband signal. Note that the delay is measured at the AM (transponder) system clock. therefore,

. The master receives the transponder transmissions and uses the RF back-end quadrature mixer 200, the digital I and Q filters 210 and 230, and the digital quadrature oscillator 220 to re- (See FIG. 3C).

The master then calculates the phase difference between F ₁ and F ₂ in the received (i.e., recovered) baseband signal using the multi-path mitigation processor arctan block 250 and the phase comparison block 255 do. The amplitude values are derived from the RF back-end RSSI block 240.

에 비례하는 순서에 있을 수 있으며, 여기에서 N은 진폭 및 위상 차 값들이 취해졌을 때(즉, 결정되었을 때) 인스턴스들의 수이다.In order to improve the estimation accuracy, it is always desirable to improve the SNRs of the amplitude estimates from block 240 and the phase difference estimates from block 255. In a preferred embodiment, the multi-path mitigation processor computes amplitude and phase difference estimates for many time instances over the distance measurement signal frequency component duration ( _Tf ). These values improve the SNR when averaged. The SNR improvement

, Where N is the number of instances when the amplitude and phase difference values are taken (i.e., determined).

및

포맷으로 변환될 수 있다. Another approach to SNR improvement is to determine amplitude and phase difference values by applying matching filter techniques over time. However, another approach is to sample and integrate them over a period (T &_lt; / RTI &_gt; T _f ) for the base-band ranging signal frequency components originally (i.e., transmitted by the master / reader) in I / (I.e., repeated) baseband ranging signal frequency components. Integration has the result of averaging multiple instances of amplitude and phase in I / Q format. The phase and amplitude values are then stored in I / Q format

And

Format.

Assume that the master baseband processor (both at FPGA 150) under the master's multi-path processor control at t = 0 starts a base-band distance measurement sequence.

,

,

.From here

.

The phase at the DAC (s) 120 and 125 outputs of the master is:

)을 갖는다는 것을 주의하자.The DACs 120 and 125 are coupled to an internal propagation delay (< RTI ID = 0.0 >

). ≪ / RTI >

)을 도입할 것이다.Similarly, the transmitter circuit components 15,30, 40, and 50 may provide additional delay (e.g.,

).

As a result, the phase of the RF signal transmitted by the master can be calculated as follows:

)를 경험한다.The RF signal from the master M is phase shifted as a function of the multi-path phenomena between the master and the tag

).

값들은 송신된 주파수들, 예로서 F₁ 및 F₂에 의존한다. 트랜스폰더(AM) 수신기는 수신기의 RF 부분의 제한된(즉, 좁은) 대역폭 때문에 각각의 경로를 분해할 수 없다. 따라서, 특정한 시간, 예를 들면 1 마이크로초(~300미터의 플라이트에 상응하는) 후, 모든 반사된 신호들이 수신기 안테나에 도착하였을 때, 다음의 공식들이 적용된다:

The values are dependent on the transmitted frequencies, for example F ₁ and F ₂ . Transponder (AM) receivers can not decompose each path because of the limited (i.e., narrow) bandwidth of the RF portion of the receiver. Therefore, when all the reflected signals arrive at the receiver antenna after a certain time, for example 1 microsecond (corresponding to a flight of ~ 300 meters), the following formulas apply:

At the AM (transponder) receiver, element 85, output, e.g., the first IF, in the first down-converter, the phase of the signal is:

)은 시스템 클록에 의존하지 않는다는 것을 주의하자. RF 프론트-엔드 필터들 및 증폭기들(요소들(95 내지 110 및 125))을 통과한 후, 제1 IF 신호는 RF 백-엔드 ADC(140)에 의해 샘플링된다. ADC(140)는 입력 신호(예로서, 제1 IF)를 언더-샘플링한다는 것이 가정된다. 따라서, ADC는 또한 제2 IF를 생성하는 하향-변환기처럼 동작한다. 제1 IF 필터들, 증폭기들 및 ADC는 전파 지연 시간을 부가한다. ADC 출력(제2 IF)에서:The propagation delays in the receiver RF section (elements 15 and 60 through 85)

) Does not depend on the system clock. After passing through the RF front-end filters and amplifiers (elements 95-110 and 125), the first IF signal is sampled by the RF back-end ADC 140. It is assumed that the ADC 140 under-samples the input signal (e.g., the first IF). Thus, the ADC also operates as a down-converter that generates a second IF. The first IF filters, amplifiers and ADC add propagation delay time. At the ADC output (2nd IF):

At the FPGA 150, the second IF signal (from the ADC output) is filtered by the RF back-end digital filters 190 and the third down-converter (i.e., the quadrature mixer 200, the digital filters 230, Converted to a base-band distance measurement signal again by a digital orthogonal oscillator (e.g., digital orthogonal oscillator 210 and digital orthogonal oscillator 220), summed in a summer 270 and stored in the memory 170. In a third down-converter output (i.e., an orthogonal mixer):

)은 시스템 클록에 의존하지 않는다는 것을 주의하자.The propagation delay in the FIR section 190

) Does not depend on the system clock.

)임을 주의하자.After the RX- > TX delay, the base-band distance measurement signal stored in the memory (M 170) is retransmitted. RX-> TX delay (

).

)를 경험한다. 상기 논의된 바와 같이, 이러한 위상 시프트는 모든 반사된 신호들이 마스터의 수신기 안테나에 도착하였을 때 특정한 시간대 후 발생한다:Until the signal from the transponder reaches the receiver antenna of the master (M), the RF signal from the transponder (AM) is another phase shift

). As discussed above, this phase shift occurs after a certain period of time when all the reflected signals arrive at the master's receiver antenna:

At the master receiver, the signal from the transponder undergoes the same down-conversion process as at the transponder receiver. The result is a reconstructed base-band distance measurement signal originally transmitted by the master.

For the first frequency component F ₁ :

For the second frequency component F2:

Substitutions:

Where T _{D_M-AM} is the propagation delay from the master (M) and transponder (AM) circuits.

은 시간(t=0)에서, ADC(들)를 포함하여, 마스터(M) 및 트랜스폰더(AM) 주파수 믹서들로부터의, LO 위상 시프트이다.From here:

Is the LO phase shift from the master (M) and transponder (AM) frequency mixers, including the ADC (s), at time (t = 0).

Also:

For the first frequency component F1:

The first frequency component F1 continues:

Second frequency component F2:

Second frequency component (F2) continued:

Additional substitutions:

Where α is a constant.

Thereafter, the final phase equation is:

(5)

(5)

From equation (5):

는

와 같다.Where i = 2, 3, 4 ........; And

The

.

):For example, the difference in time instances t1 and t2 (

):

차이를 찾기 위해, 우리는

을 알 필요가 있다:

To find the difference, we

You need to know:

및

은 루프-백 모드에 디바이스들을 둠으로써 측정되는 마스터(M) 및 트랜스폰더(AM) TX 및 RX 회로들을 통한 전파 지연들이다. 마스터 및 트랜스폰더 디바이스들은 자동으로

및

을 측정할 수 있으며; 우리는 또한 t_RTX 값을 안다.From here

And

Are propagation delays through the master (M) and transponder (AM) TX and RX circuits measured by placing devices in a loop-back mode. Master and transponder devices automatically

And

Can be measured; We also know the t _RTX value.

값이 다음과 같이 발견될 수 있다:From the formulas and the t _RTX value, T _{D - M - AM} can be determined and consequently for given t ₁ and t ₂ ,

The value can be found as follows:

(6)

(6)

을 가정하면:or,

Assuming:

(6A)

(6A)

From equation (6), it can be concluded that at the operating frequency (s), the distance measurement signal (s) complex amplitude values can be found from processing the returned base-band distance measurement signal.

)은 서브스페이스 알고리즘들이 일정한 위상 오프셋에 민감하지 않기 때문에 0과 같은 것으로 추정될 수 있다. 필요하다면,

값(위상 초기 값)은 여기에 전체적으로 참조로서 통합된, 미국 특허 번호 제7,561,048호에 설명된 바와 같이 협-대역폭 거리 측정 신호 방법을 사용하여 TOA(도착 시간)을 결정함으로써 발견될 수 있다. 이러한 방법은

과 같은, 거리 측정 신호 왕복 지연을 추정하며,

값은 다음의 식으로부터 발견될 수 있다:The initial phase value (

) Can be estimated to be equal to 0 since the subspace algorithms are not sensitive to a constant phase offset. If you need,

The value (phase initial value) can be found by determining the TOA (arrival time) using the narrow-bandwidth distance measurement signal method as described in U.S. Patent No. 7,561,048, incorporated herein by reference in its entirety. This method

And estimates the round trip delay of the distance measurement signal,

The value can be found from the following equation:

,

,

or:

)은 다중-경로 프로세서의 arctan 블록(250)에 의해 산출된다. SNR을 개선하기 위해, 다중-경로 완화 프로세서 위상 비교 블록(255)은 식(6A)을 사용하여 많은 인스턴스들(n(n=2, 3, 4 ............)에 대한

을 산출하며, 그 후 SNR을 개선하기 위해 그것들의 평균을 낸다.

을 주의하자.In the preferred embodiment, the returned base-band distance measurement signal phase values (

Is computed by the arctan block 250 of the multi-path processor. To improve the SNR, the multi-path mitigation processor phase comparison block 255 computes a number of instances n (n = 2, 3, 4 ...) using equation (6A). ) For

, And then averages them to improve the SNR.

Be careful.

From equations 5 and 6 it becomes apparent that the recovered (i.e., received) base-band distance measurement signal has the same frequency as the original base-band signal transmitted by the master. Thus, despite the fact that the master (M) and transponder (AM) system clocks may be different, there is no frequency translation. Since the base-band signal is composed of several frequency components, each component consisting of multiple periods of a sinusoid, the frequency of the respective original (i.e., transmitted by the master) It is also possible to estimate the phase and amplitude of the received distance measurement signal by sampling the component frequencies of each received base-band signal with components and integrating the resulting signal over a period (T &_lt; = T _f ).

)을 발생시킨다. 마스터에 의해 전송된 각각의 기저-대역 신호 개개의 주파수 성분은 시간이 T_{D_M-AM}만큼 시프트되어야 한다는 것을 주의하자. 통합 동작은 진폭 및 위상(예로서, SNR을 증가시키는)의 다수의 인스턴스들의 평균을 낸 결과를 생성한다. 위상 및 진폭 값들은 I/Q 포맷에서

및

포맷으로 변환될 수 있다는 것을 주의하자. This operation may be performed using complex amplitude values of the distance measurement signal received in I / Q format (

). Note that the frequency components of each base-band signal transmitted by the master _SHOULD be shifted in time by _{TD D-AM} . The aggregate operation produces a result that averages multiple instances of amplitude and phase (e.g., increasing SNR). The phase and amplitude values are stored in the I / Q format

And

Format. ≪ / RTI >

및

포맷으로의 후속 변환의 이러한 방법은 도 3c에서 위상 비교 블록(255)에서 구현될 수 있다. 따라서, 블록의 255 설계 및 구현에 의존하여, 식 (5)에 기초한, 바람직한 실시예의 방법, 또는 이 섹션에 설명된, 대안적인 방법이 사용될 수 있다.Sampled, integrated over a period of T &_lt; = T _f ,

And

This method of subsequent conversion into a format can be implemented in the phase comparison block 255 in Figure 3c. Thus, depending on the design and implementation of block 255, the method of the preferred embodiment, based on equation (5), or an alternative method described in this section, can be used.

Although the distance measurement signal bandwidth is narrow, the frequency difference (f _n - f _l ) can be relatively large, e.g., approximately several megahertz. As a result, the bandwidth of the receiver should be kept wide enough to carry all of the components of the f _l : f _n distance measurement signal frequencies. This wide receiver bandwidth affects SNR. In order to reduce the receiver effective bandwidth and improve the SNR, the received distance measurement signal base-band frequency components are transformed by digital narrow-bandwidth filters tuned for each individual frequency component of the received base- May be filtered by the RF back-end processor at block 150. However, these multiple digital filters (the number of filters being equal to the number n of individual frequency components) add additional burden to the FPGA resources, thereby increasing its cost, size and power consumption.

In the preferred embodiment, only two narrow bandwidth digital filters are used: one filter is always tuned for the f ₁ frequency component and the other filter is tuned for all other frequency components f ₂ : f _n . . Multiple instances of the distance measurement signal are transmitted by the master. Each instance consists of only two frequencies (f ₁ : f ₂ ; f ₁ : f ₃ .....; f _i .....; f ₁ : f _n ). Similar strategies are also possible.

Note that by adjusting the frequency synthesizers, it is also entirely possible to keep the base-band ranging signal components at only two (or even one) generating the remainder of the frequency components by changing K _SYN as an example. Upconverters and Downconverters The LO signals for the mixers are preferably generated using direct digital synthesis (DDS) techniques. For high VHF band frequencies, this can give an undesirable burden to the transceiver / FPGA hardware. However, for lower frequencies, this may be a useful approach. Analog frequency synthesizers can also be used, but may take additional time to determine after the frequency has changed. Also, in the case of analog synthesizers, two measurements at the same frequency must be made to cancel the phase offset that may occur after changing the frequency of the analog synthesizer.

이 산출될 때, 양쪽: T_{LB _AM} 및 t_RTX는 마스터(M) 클록에서 측정(카운팅)된다. 이것은 에러를 도입한다:The actual _{TD_M-AM} used in the above equations is _measured in both the master (M) and transponder (AM) system clocks, for example T _{LB _AM} and t _RTX are counted in the transponder (AM) T _{LB _M} is counted at the master (M) clock. But,

T _{LB _AM} and t _RTX are measured (counted) at the master (M) clock. This introduces an error:

(7)

(7)

Phase estimation error (7) affects accuracy. Therefore, it is necessary to minimize such errors. If β ^M = β ^AM , in other words, if all the master (s) and transponders (tags) system clocks are synchronized, the contribution from t _RTX time is eliminated.

In a preferred embodiment, the master and transponder units (devices) can synchronize clocks with any of the devices. For example, a master device may act as a reference. Clock synchronization is realized by using a remote control communication channel, whereby under the control of the FPGA 150, the frequency of the temperature compensated crystal oscillator (TCXO) 20 is adjusted. The frequency difference is measured at the output of the master device's summer 270 while the selected transponder device transmits the carrier signal.

The master then sends a command to the transponder to increase / decrease the TCXO frequency. This procedure may be repeated several times to achieve greater accuracy by minimizing the frequency at the summer 270 output. Note that in an ideal case, the frequency at the output of the summer 270 should be equal to zero. An alternative is to measure the frequency difference and calibrate the estimated phase without adjusting the TCXO frequency of the transponder.

? ^M- ? ^AM can be significantly reduced, but there is a phase estimation error when? ^M ? 1. In this case, the margin of error depends on the long term stability of the reference device (usually the master (M)) clock generator. Also, the process of clock synchronization has a large number of units, especially in the field, and can take a considerable amount of time. During the synchronization process, the tracking-locating system becomes partially or completely inoperable, which negatively impacts system readiness and performance. In this case, the above-mentioned method which does not require the TCXO frequency adjustment of the transponder is preferred.

Commercially available (off-the-shelf) TCXO components have a high degree of accuracy and stability. Specifically, the TCXO components for GPS commercial applications are very accurate. With these devices, the phase error effect on the latitude accuracy can be less than one meter without the need for frequency clock synchronization.

)을 획득한 후, 추가 프로세싱(즉, 초-분해능 알고리즘들의 실행)이 다중-경로 완화 프로세서의 일부인, 소프트웨어-기반 구성요소에서 구현된다. 이러한 소프트웨어 구성요소는 FPGA(150)(도시되지 않음)에 내장되는 마스터(판독기) 호스트 컴퓨터 CPU 및/또는 마이크로프로세서에서 구현될 수 있다. 바람직한 실시예에서, 다중-경로 완화 알고리즘(들) 소프트웨어 구성요소는 마스터 호스트 컴퓨터 CPU에 의해 실행된다. Narrow bandwidth distance measurement signal Multipath path mitigation The processor returns the narrowband distance measurement signal complex amplitude (

), And then the additional processing (i.e., the execution of the super-resolution algorithms) is implemented in a software-based component that is part of a multi-path mitigation processor. Such software components may be implemented in a master (reader) host computer CPU and / or microprocessor embedded in an FPGA 150 (not shown). In a preferred embodiment, the multi-path mitigation algorithm (s) software component is executed by the master host computer CPU.

The super-resolution algorithm (s) generates an estimate, e.g.,? _K values, of (2? X? _K ) "frequencies". In the final stage, the multi-path mitigation processor selects < RTI ID = 0.0 ># with < / RTI > the minimum value (i.

In certain cases where the distance measurement signal narrow bandwidth requirements are somewhat relaxed, the DLOS path may be separated from the MP paths by using a continuous (temporal) chirp. In a preferred embodiment, this continuous chirp is Linear Frequency Modulation (LFM). However, other chirp waveforms may also be used.

라디안의 처프 레이트를 제공한다. 다수의 처프들이 송신되며 다시 수신된다. 처프 신호들은 동일한 위상에서 시작된 각각의 처프를 갖고 디지털로 발생된다는 것을 주의하자.Under multi-path mitigation processor control, assume that a chirp with a bandwidth of B and a duration of T is transmitted. It is per second

Provides radian chirp rate. A number of chirps are transmitted and received again. Note that the chirp signals are digitally generated with each chirp starting at the same phase.

In a multi-path processor, each received single chirp is aligned so that the returned chirp comes from the middle of the region of interest.

The chirp waveform is:

이며, 여기에서 ω₀은 0<t<T에 대한 초기 주파수이다.

Where ω ₀ is the initial frequency for 0 <t <T.

For a single delayed round trip τ, for example a multi-path, the returned signal (chirp) is s (t-τ).

The multi-path mitigation processor then "deramps " s (t-tau) by performing complex conjugate mixing with the original transmitted chirp. The resulting signal is a complex sinusoid:

(8)

(8)

는 진폭이며 2βτ은 주파수 및 0≤t≤T이다. 마지막 항은 위상이며 그것은 무시해도 될 정도임을 주의하자.From here

2? T is the frequency and 0? T? T. Note that the last term is phase and it is negligible.

In the case of a multi-path, the composite demodulating signal consists of a number of complex sinusoids:

(9)

(9)

Where L is the number of distance measurement signal paths, including the DLOS path and 0? T? T.

Multiple chunks are transmitted and processed. Each chirp is individually processed and / or processed as described above. The multi-path mitigation processor then assembles the results of the individual chirp processing:

(10)

(10)

는 두 개의 연속 처프들 사이에서의 데드 시간 구역이고; 2βτ_k는 인공 지연 "주파수들"이다. 다시, 가장 흥미로운 것은 최저 "주파수"이며, 이것은 DLOS 경로 지연에 대응한다. Where N is the number of chirps,

Is the dead time zone between two successive chirps; 2βτ _k is the "frequency" artificial delay. Again, the most interesting is the lowest "frequency &quot;, which corresponds to the DLOS path delay.

은 때로는 복소 정현파들의 합의 N개의 샘플들로서 여겨질 수 있다:In equation (10)

Can sometimes be thought of as N samples of the sum of the complex sinusoids:

Thus, the number of samples may be a multiple of N, e.g., α N; α = 1, 2,.

From equation (10), the multi-path mitigation processor generates alpha N complex amplitude samples in the time domain used for further processing (i.e., execution of super-resolution algorithms). This additional processing is implemented in a software component that is part of a multi-path mitigation processor. These software components may be executed by a master (reader) host computer CPU and / or by a microprocessor embedded in the FPGA 150 (not shown), or both. In a preferred embodiment, the multi-path mitigation algorithm (s) software is executed by the master host computer CPU.

Super-resolution algorithm (s) is an estimate, for a 2βτ _k "frequencies" and generates the value τ _k. In the final stage, the multi-path mitigation processor selects a value with a minimum value, i.e., DLOS delay time.

The description will be given to a specific processing method, referred to as the " threshold technique, " which may serve as an alternative to the super-resolution algorithms. In other words, it is used to enhance reliability and accuracy in distinguishing DLOS paths from other MP paths using artificially generated composite wider bandwidth distance measurement signals.

The frequency domain base-band distance measurement signal shown in Figures 1 and 1a can be converted to a time domain base-band signal s (t)

(11)

(11)

It is easily verified that s (t) is periodic with a period (1 /? t), and for any integer (k), the peak value of the signal, s (k /? t) = 2N + 1. Here, in Fig. 1 and Fig. 1A, n = N.

Fig. 4 shows two periods of s (t) for the case of N = 11 and DELTA f = 250 kHz. The signal appears as a sequence of pulses of height (2N + 1 = 23) separated by 1 /? F = 4 microseconds. There are sinusoidal waveforms with variable amplitudes and 2N zeros between the pulses. The optical bandwidth of the signal may be due to the narrowness of the high pulses. It can also be appreciated that the bandwidth extends from the 0 frequency to N? F = 2.75 MHz.

The basic idea of the threshold value method used in the preferred embodiment is to enhance the wider bandwidth distance measurement reliability and accuracy of an artificially generated composite when distinguishing DLOS paths from other MP paths. The threshold method detects when the beginning of the leading edge of the broadband pulse arrives at the receiver. Due to filtering at the transmitter and receiver, the leading edge does not instantaneously rise, but raises the noise with a smoothly increasing slope. The TOA of the leading edge is measured by detecting when the leading edge crosses a predetermined threshold (T).

The small threshold is desirable because it crosses faster and the error delay (tau) between the actual start and critical crossing of the pulse is small. Thus, any pulse replica arriving due to the multi-path will have no effect if the start of the replica has a delay greater than?. However, the presence of noise places a limit on how small the threshold value T can be. One way to reduce the delay tau is to use the derivative of the received pulse instead of the pulse itself because the derivative increases more rapidly. The second derivative has a much faster increase. Higher order derivatives can be used, but in practice they can raise the noise level to an unacceptable value, so the limit second order derivative is used.

The 2.75 MHz wide signal depicted in FIG. 4 has a very wide bandwidth, but it is not suitable for measuring range by the above-mentioned method. The method requires transmit pulses each having an O-signal precursor. However, it is possible to achieve the object by modifying the signal so that the sinusoidal waveform between the pulses is essentially erased. In a preferred embodiment, it is done by constructing a waveform that closely approximates the signal on the selected interval between high pulses, and then subtracting it from the original signal.

The technique can be illustrated by applying it to the signal in FIG. The two black dots shown on the waveform are the endpoints of the centered interval (I) between the first two pulses. The left and right endpoints of interval I, which have been experimentally determined to provide the best results, are the following:

(12)

(12)

Attempts are made to generate a function g (t) that essentially erases the signal s (t) on this interval, but does not cause much harm outside the interval. The expression (11) indicates that the sine (t) is sinusoidal wave (sin (2N + 1)? Ft) modulated by 1 / sin? (h (t)) is found, and then g (t) is formed as a product:

(13)

(13)

h (t) is generated by the sum of:

(14)

(14)

From here

(15)

(15)

And the coefficients a _k are selected to minimize the minimum-squared error over the interval I

. (16)

. (16)

The solution is easily obtained by taking the partial derivatives of J for a _k and setting them equal to zero. The result is a linear system of M + 1 equations.

(17)

(17)

It can be solved for a _k , where

(18)

(18)

After that,

(19)

(19)

(T) &lt; / _RTI &gt; given by equation (12)

(20)

(20)

g (t) is subtracted from s (t) to obtain the function r (t), which essentially has to cancel s (t) on interval I. As indicated in the appendix, the appropriate choice for the upper bound (M) for summation in equation (20) is M = 2N + 1. Using these values and the results from the appendix,

(21)

(21)

From here

(22)

(22)

From equation (17) it is understood that a total of 2N + 3 frequencies (including 0-frequency DC terms) are required to obtain the desired signal r (t). Figure 5 shows the resulting signal r (t) for the original signal s (t) shown in Figure 1, where N = 11. In this case, the configuration of r (t) requires 25 carriers (including DC term (b ₀ )).

The important characteristics of r (t) as constructed above are:

1. As can be seen from (14), the lowest frequency is 0 Hz and the highest frequency is (2N + 1) Δf Hz. Thus, the total bandwidth is (2N + 1) DELTA f Hz.

)에 위치된 사인 함수인, 하나의 캐리어를 제외하고, △f 이격된 코사인 함수들(DC를 포함한)이다.2. All carriers are frequency (

(Including DC), except for one carrier, which is a sine function located at? F.

3. The original signal s (t) has a period (1 /? F), but r (t) has a period (2 /? F). The first half-period of each period of r (t), which is the total period of s (t), contains the erased portion of the signal, and the second half of r (t) is the large vibration segment. Thus, the erasure of the precursor occurs only at every other period of s (t).

This occurs because the erase function g (t) actually enhances s (t) in all other periods of s (t). The reason is that g (t) reverses its polarity at every peak of s (t), whereas s (t) is not. The method of making all the cycles of s (t) includes the erased portion to increase the processing gain by 3 dB as described below.

4. The length of the erased portion of s (t) is about 80% to 90% of 1 /? F. Therefore,? F requires this length to be small enough to make it sufficiently long to remove any residual signal from previous non-zero portions of r (t) due to multi-path.

5. Each zero part of r (t) immediately following is the first cycle of the vibrating part. In a preferred embodiment, in the TOA measurement method as described above, the first half of this cycle is used to measure the TOA, specifically the beginning of its ascent. It is interesting to note that this first half-cycle peak value (referred to as the main peak) is somewhat larger than the corresponding peak of s (t) located at about the same time point. The width of the first half-cycle is approximately inversely proportional to N? F.

6. A large amount of processing gain can be achieved by:

(a) Let us use iterations of the signal r (t), since r (t) is periodic with period (2 / Δf). In addition, an additional 3 dB of processing gain is possible by the method to be described later.

(b) Narrowband filtering. Because each of the 2N + 3 carriers is a narrowband signal, the occupied bandwidth of the signal is much smaller than that of a broadband signal spread over the entire allocated band of frequencies.

For the signal r (t) shown in Fig. 5, the length of the erased portion of s (t) is about 3.7 microseconds or 1110 meters, where N = 11 and DELTA f = 250 kHz. This is too much to remove any residual signal from previous non-zero portions of r (t) due to the multi-path. The main peak has a value of approximately 35, and the maximum size in the precursor (i.e., erase) region is approximately 0.02, which is below the main peak 65 dB. This is desirable for obtaining good performance using the TOA measurement limit technique as described above.

마이크로초이며, 여기에서 기간은 8 마이크로초이다. 이 예는 단위 시간당 보다 많은 주기들을 가지므로, 그것은 보다 많은 프로세싱 이득이 달성될 수 있다고 예상할 수 있다.The use of fewer carriers is depicted in Fig. 6, which shows that for a total of only 2N + 3 = 9 carriers, the signal generated using? F = 850 kHz, N = 3, and M = 2N + 1 = For example. In this case, the period of the signal is only &lt; RTI ID = 0.0 &gt;

Microseconds, where the duration is 8 microseconds. Since this example has more cycles per unit time, it can be expected that more processing gain can be achieved.

However, since fewer carriers are used, the amplitude of the main peak is about 1/3 of the former, which tends to erase the expected additional processing gain. Also, the length of the zero-signal precursor segments is shorter, about 0.8 microseconds or 240 meters. This should still be sufficient to remove any residual signal from previous non-zero portions of r (t) due to the multi-path. Note that the total bandwidth of (2N + 1) [Delta] f = 5.95 MHz is approximately the same as before, and the half-cycle width of the main peak is also approximately the same. As fewer carriers are used, there must be some additional processing gain when each carrier is narrowband filtered at the receiver. In addition, the maximum size in the precursor (i.e., erase) region is now about 75 dB below the main peak, which is a 10 dB improvement from the previous example.

Transmission at RF frequencies: Up to this point r (t) has been described as a baseband signal for simplicity. However, it can be converted to RF, transmitted, received, and then reconstructed as a baseband signal at the receiver. To illustrate, frequency components (r (t)) in the baseband signal r (t) traveling through one of the multi-path propagation paths with index j (radians / second frequencies used for notation simplification) ω _k ):

b _k cos? _k t (from transmitter to baseband)

b _k cos (? +? _k ) t (converted to RF by the frequency (?))

(at the receiver antenna) a _j b _k cos [(ω + ω _k ) (t - τ _j ) + φ _k ]

_{a j b k cos [ω k} (t-τ j) + φ j + θ] ( by the frequency (-ω) conversion to baseband) 23

It is assumed here that the transmitter and the receiver are frequency synchronized. The parameter b _k is the k-th coefficient in equation (21) for r (t). The parameters? _J and? _J are the path delay and phase shift (due to the dielectric properties of the reflector) of the j propagation path, respectively. The parameter [theta] is the phase shift that occurs in down-conversion from the receiver to the baseband. Functions of a similar sequence may be provided for the sine component of equation (21).

It is important to note that the final baseband signal in equation (20) will still have zero-signal precursors, as long as the zero-signal precursors at r (t) have a length sufficiently larger than the most significant propagation delay Do. Of course, when all the frequency components (index k) are combined on all paths (index j), the baseband signal at the receiver includes all phase shifts, including the distorted version of r (t) will be.

Sequential carrier transmissions and signal reconstruction are illustrated in Figures 1 and 1A. It is assumed that the transmitter and receiver are time and frequency synchronized, and 2N + 3 transmit carriers need not be transmitted simultaneously. As an example, consider the transmission of a signal in which the base-band representation is as shown in Figs. 1A and 6.

In FIG. 6, assume that each of nine frequency components is sequentially transmitted for N = 3 and 1 millisecond. The start and end times for each frequency transmission are known at the receiver so that it can sequentially start and end reception of each frequency component at each of these times. Since the signal propagation time is very short compared to 1 millisecond (which would typically be a few microseconds in an intended application), a small fraction of each received frequency component should be ignored and the receiver can easily erase it.

주 피크들로부터 이용 가능한 부가적인 프로세싱 이득이 있을 것이다. The overall process of receiving nine frequency components may be repeated in additional reception of 9-millisecond blocks to increase the processing gain. At 1 second of total receive time, there will be about 111 such 9-millisecond blocks available for processing gain. Additionally, within each block,

There will be additional processing gain available from the main peaks.

In general, it is not important that signal reconstruction can be made very economical and will inherently allow all possible processing gains. For each of the 2N + 3 receive frequencies:

1. Measure the phase and amplitude of each 1-millisecond reception of the frequency to form a sequence of stored vectors (phasers) corresponding to the frequency.

2. Average the stored vectors for the frequency.

3. Finally, we use 2N + 3 vector averages for 2N + 3 frequencies to reconstruct one period of the baseband signal with duration (2 /? F), and reconstruct to estimate the signal TOA Let's use.

This method is not limited to 1-millisecond transmissions, and the length of transmissions may be increased or decreased. However, the total time for all transmissions must be sufficiently short to stop any motion of the receiver or transmitter.

By simply reversing the polarity of the erase function g (t), it is possible to erase between the peaks of s (t), where r (t) has previously oscillated. However, in order to obtain an erasure between all peaks of s (t), the function g (t) and its polarity reversed version must be applied at the receiver, which involves a weighting factor at the receiver.

Coefficients weighting at the receiver: If desired, the coefficients b _k in equation (21) may be used at the transmitter for the construction of r (t) and introduced at the receiver instead. This is easily understood by considering the sequence of the signals in Equation (20), where b _k is introduced at the end instead of at the beginning. Ignoring the noise, the values are:

cos? _k t (at baseband in transmitter)

cos (? +? _k ) t (converted to RF by the frequency (?))

a _j cos [(ω + ω _k ) (t-τ _j ) + φ _j ] (at the receiver antenna)

(transformed to baseband by frequency (-ω)) and a _j cos [(ω _k (t-τ _j ) + φ _j +

(24), which is weighted by the coefficient (b _k ) in the baseband, a _j b _k cos [ω _k (t - τ _j ) + φ _j +

The transmitter can then transmit all frequencies with the same amplitude, which simplifies its design. It should be noted that this method also adds noise at each frequency, and its effect must be taken into account. It should also be noted that the coefficient weighting must be done at the receiver to effect the polarity reversal of g (t) to obtain twice as many available main peaks.

Scaling of? F to center frequencies in channels: To meet FCC requirements at frequencies below VHF, a channelized transmission with a constant channel spacing will be required. In a channelized transmission band with a small constant channel spacing compared to the total allocated bandwidth, which is the case for sub-VHF frequencies band (s), small adjustments for? F may be made, if necessary, Allowing them to be in channel centers without substantially changing performance from the design values. In the two examples of previously provided baseband signals, all frequency components are multiples of [Delta] f / 2 so that if the channel interval divides [Delta] f / 2, then the lowest RF transmit frequency can be centered in one channel All other frequencies fall off the center of the channels.

In some radio frequency (RF) -based identification, tracking and locating systems, both in addition to performing distance measurement functions, the master unit and the tag unit also perform voice, data, and control communication functions. Similarly, in the preferred embodiment, both the master unit and the tag perform voice, data and control communication functions in addition to the distance measurement function.

According to a preferred embodiment, the distance measurement signal (s) are subject to extensive sophisticated signal processing techniques, including multi-path mitigation. However, these techniques may not provide themselves with voice, data and control signals. As a result, the operating range of the proposed system (as well as other existing systems) is out of range during voice and / or data and / or control communications, not by its ability to reliably and accurately measure distances Lt; / RTI &gt;

In other radio frequency (RF) -based identification, tracking and location systems, the distance measurement function is separate from voice, data and control communication functions. In these systems, separate RF transceivers are used to perform voice, data and control communications functions. The disadvantages of this approach are increased system cost, complexity, and size.

In order to avoid the above-mentioned disadvantages, in a preferred embodiment, the narrow bandwidth distance measurement signal or the base-band narrow bandwidth distance measurement signal several individual frequency components have the same data / control signals and, in the case of speech, Packets are modulated with data. At the receiver, individual frequency components with the highest signal strength are demodulated and the information reliability obtained can be further enhanced by performing "voting" or other signal processing techniques using information redundancy.

This approach allows to avoid "null" phenomena, where incoming RF signals from multiple paths combine destructively with the DLOS path and thus significantly reduce the received signal strength and reduce its SNR Lt; / RTI &gt; In addition, this method allows incoming signals from multiple paths to look up a DLOS path and a set of frequencies that constructively combine with each other, thus increasing the received signal strength and being associated with its SNR.

As previously mentioned, spectral estimation-based super-resolution algorithms generally use the same model: a linear combination of complex exponents of frequencies and their complex amplitudes. This complex amplitude is given by Equation 3 above.

All spectral estimation-based super-resolution algorithms require a priori knowledge of the number of complex indices, i.e. the number of multipath paths. The number of such complex indices is called the model size and is determined by the number of multi-path components L as shown in equations (1) - (3). However, when estimating the path delay, which is the case for RF tracking-location applications, this information is not available. This adds another dimension, or model size estimate, to the spectral estimation process through the super-resolution algorithms.

It is shown that in the case of model size underestimation, the accuracy of frequency estimation is affected and when the model size is overestimated, the algorithm generates spurious, e.g., non-existent, frequencies (Kei Sakaguchi et al., ESPRIT- Effect of Model Order Estimation Error on the. Conventional methods of model size estimation, such as AIC (Akaiike Information Standard), MDL (Minimal Description Length), etc., have a high sensitivity to correlation between signals (complex indices). But in the case of RF multipath, this is always the case. More precisely, for example, there will always be a positive correlation remaining after the forward-backward smoothing algorithms are applied.

In the Sakaguchi paper, it is important to distinguish real frequencies (signals) from spurious frequencies (signals) by using an overestimated model and estimating their signal power (amplitude) and then rejecting signals with very low power Is proposed. This method is an improvement over the existing methods, but it is not guaranteed. The inventors implemented the Kei Sakaguchi et al. Method and drove simulations for more complex cases with larger model sizes. It has been observed that in some cases, the spurious signal may have an amplitude very close to the actual signal amplitude.

All spectral-estimation-based super-resolution algorithms operate by separating the incoming signal complex amplitude data into two sub-spaces: noise sub-space and signal sub-space. If these sub-spaces are properly defined (separated), the model size is equal to the signal sub-space size (dimension).

In one embodiment, model size estimation is accomplished using "F" statistics. For example, for the ESPRIT algorithm, the singular value decomposition of the estimates of the covariance matrix (with forward / backward correlation smoothing) is ordered in ascending order. Thereafter, division is performed whereby the (n + 1) eigenvalues are divided into the n-th eigenvalues. This ratio is the "F" random variable. The worst case is the "F" random variable of (1,1) degrees of freedom. The 95% confidence interval for an "F" random variable with (1,1) degrees of freedom is 161. Setting the value to the threshold determines the model size. Also note that for noise subspaces, the eigenvalues represent an estimate of the noise power.

This method of applying the "F" statistics to the ratio of the eigenvalues is a more accurate method of estimating the model size. It should be noted that other degrees of freedom in the "F" statistics can also be used for threshold calculation and consequently model size estimation.

Nonetheless, in some cases, two or more very closely spaced (temporally) signals may revert to one signal due to real-world measurement faults. As a result, the above-mentioned method will underestimate the number of signals, i.e. the model size. Since model size underestimation reduces frequency estimation accuracy, it is prudent to increase the model size by adding a certain number. This number may be determined empirically and / or from simulations. However, when the signals are not closely spaced, the model size will be overestimated.

In these cases, spurious, i.e., non-existent, frequencies may appear. As previously noted, using signal amplitudes for spurious signal detection is not always working because in some instances the spurious signal (s) are observed to have amplitudes very close to the actual signal (s) amplitude. Thus, in addition to amplitude identification, filters may be implemented to improve the probability of spurious frequencies removal.

The frequencies estimated by the super-resolution algorithms are artificial frequencies (Equation 2). In effect, these frequencies are individual path delays in a multipath environment. As a result, no negative frequencies should be present, and all negative frequencies generated by the super-resolution algorithm are the spurious frequencies to be rejected.

) 값들로부터 추정될 수 있다. 이들 방법들은 보다 낮은 정확도를 갖지만, 이러한 접근법은 지연들, 즉 주파수들을 식별하기 위해 사용되는 범위를 수립한다. 예를 들면, 신호 진폭(

)이 최대치, 즉 널들을 회피하는 것에 가까운 △f 간격들에서

의 비는 DLOS 지연 범위를 제공한다. 실제 DLOS 지연은 2배 이상 또는 이하까지일 수 있지만, 이것은 스퓨리어스 결과들을 거절하도록 돕는 범위를 정의한다. Moreover, the DLOS range of distances can be calculated using the complex amplitude (&lt; RTI ID = 0.0 &gt;

) &Lt; / RTI &gt; values. Although these methods have lower accuracy, this approach establishes the range that is used to identify delays, i.e., frequencies. For example, the signal amplitude (

) Is at its maximum, i.e., at intervals Δf close to avoiding nulls

/ RTI &gt; provides a DLOS delay range. The actual DLOS delay can be up to two or more times, but this defines a range that helps to reject spurious results.

In an embodiment, the distance measurement signal makes a round trip. In other words, it travels in both directions: master / reader to target / slave and target / slave to master / reader again.

, 여기에서 ω는 동작 대역에서의 동작 주파수이며 α는 톤 신호 진폭이다.The master sends a tone:

, Where [omega] is the operating frequency in the operating band and [alpha] is the tone signal amplitude.

In the target's receiver, the received signal (short-direction) is:

(25)

(25)

및

은 양 또는 음일 수 있다.Where N is the number of signal paths in the multipath environment; K0 and? ₀ are the amplitude and flight time of the DLOS signal;

And

May be positive or negative.

(26)

(26)

은 주파수 도메인에서 단방향 다중경로 RF 채널 전달 함수이며; A(ω)≥0.From here:

Is a unidirectional multipath RF channel transfer function in the frequency domain; A (ω) ≥0.

The target retransmits the received signal:

(27)

(27)

At the master receiver, the round trip signal is:

or:

(28)

(28)

From the equations (26) and (28) on the other hand:

(29)

(29)

은 주파수 도메인에서 왕복 다중경로 RF 채널 전달 함수이다.From here:

Is a reciprocating multipath RF channel transfer function in the frequency domain.

표현이 이들 경로 지연들의 조합들, 예를 들면: τ₀+τ₁, τ₀+τ₂...., τ₁+τ₂, τ₁+τ₃, ..., 등을 포함하기 때문에 단-방향 채널 다중경로보다 많은 수의 경로들을 가진다. From equation (29), the round-trip multi-path channel is characterized by τ ₀ ÷ τ _N path delays

Since the expression includes combinations of these path delays, e.g., τ ₀ + τ ₁ , τ ₀ + τ ₂ ...., τ ₁ + τ ₂ , τ ₁ + τ ₃ , ..., And has more paths than the single-directional channel multipath.

These combinations dramatically increase the number of signals (complex indices). Therefore, the probability of very closely spaced (temporal) signals will also increase and lead to considerable model size underestimation. Thus, it is desirable to obtain a single-direction multipath RF channel transfer function.

)은 직접 측정될 수 없다. 왕복 위상 측정 관측으로부터 단-방향의 위상을 결정하는 것이 가능하다:In the preferred embodiment, the short-direction amplitude values ( ) Can be obtained directly from the target / slave device. However, the short-direction phase values (

) Can not be directly measured. It is possible to determine the phase in the short-direction from the reciprocal phase measurement observation:

및

And

이도록 하는 위상(α(ω))의 두 개의 값들이 있다.However, for each value of omega,

([Omega]), which is the phase of the output signal.

A detailed description of solving such ambiguities is shown below. Distance Measurement Signal If the different frequency components are close to each other, the short-direction phase for most parts can be found by dividing the reciprocal phase by two. Exceptions will include regions close to "null ", where the phase may undergo significant changes even with small frequency steps. Note: "null" phenomena are cases where incoming RF signals from multiple paths combine destructively with the DLOS path and with each other, thus significantly reducing the received signal strength and associated with its SNR.

Let h (t) be the short-directional impulse response of the communication channel. The corresponding transfer function in the frequency domain is:

(30)

(30)

Where A (ω) ≥0 is the magnitude and α (ω) is the phase of the transfer function. If the short-directional impulse response is retransmitted again on the same channel as it is received, then the resulting two-way transfer function is:

(31)

(31)

Where B (ω) ≥0. Assume that the bi-directional transfer function G (?) Is known for all? At some open frequency interval (? ₁ ,? ₂ ). Is it possible to determine the one-way transfer function H (ω) defined for (ω ₁ , ω ₂ ) that produced G (ω)?

Since the magnitude of the bi-directional transfer function is the square of the magnitude of the short-direction magnitude,

(32)

(32)

However, when trying to restore the phase of the short-direction transfer function from the observation of G (?), The situation is more subtle. For each value of [omega], there are two values of the phase [alpha] ([omega]) that are to be the next.

(33)

(33)

A number of different solutions can be generated by independently selecting one of two possible phase values for each different frequency ([omega]).

The following theorems, which assume that any one-way transfer function is continuous at all frequencies, help to solve this situation.

)의 제로들을 포함하지 않는 주파수들(ω)의 개방 간격이라고 하자.

를 β(ω)=2γ(ω)인 경우 I에 대한 연속 함수라고 하자. 그 후 J(ω) 및 -J(ω)는 I에 대한 G(ω)를 생성하는 단-방향 전달 함수들이며, 다른 것들은 없다.Theorem 1: I to the positive-direction transfer function (

) &Lt; / RTI &gt; of the frequencies &lt; RTI ID = 0.0 &gt;

Let β (ω) = 2γ (ω) be a continuous function for I. J (ω) and -J (ω) are then single-direction transfer functions that produce G (ω) for I, and others are not.

)이며, 여기에서 β(ω)=2α(ω)이다. I에 대해 G(ω)≠0므로, H(ω) 및 J(ω)는 I에 대해 비제로이다. 그 후, Proof : One of the solutions to the one-way transfer function is that it is a continuous function for I

), Where? (?) = 2? (?). H (?) And J (?) Are non-zero for I, since G (?)? After that,

(34)

(34)

Since H (ω) and J (ω) are continuous and nonzero for I, their ratio is continuous for I, and therefore the right side of (34) is continuous for I. The conditions β (ω) = 2α (ω) = 2γ (ω) mean that for each ωI, α (ω) - γ (ω) is 0 or π. However, α (ω) - γ (ω) can not switch between these two values without causing discontinuity on the right side of (34). Therefore, α (ω) - γ (ω) = 0 for all ω ∈ I, or α (ω) - γ (ω) = π for all ω ∈ I. In the first case, we get J (ω) = H (ω), and in the second case we get J (ω) = - H (ω).

)의 비 제로들을 포함한 임의의 개방 간격(I)에 대한 단-방향 해법을 얻기 위해, 우리는 함수(

)를 형성하여, J(ω)를 연속적이게 하기 위한 방식으로 β(ω)=2γ(ω)를 만족시키는 γ(ω)의 값들을 선택한다는 것을 증명한다. 이러한 속성, 즉 H(ω)을 가진 해법이 있다는 것이 알려져 있으므로, 이것을 행하는 것은 항상 가능하다.This theorem applies to the transfer function (

To obtain a one-way solution for any open interval (I), including nonzero,

) To form values of γ (ω) satisfying β (ω) = 2γ (ω) in a manner to make J (ω) continuous. Since it is known that there is a solution with this property, H (ω), it is always possible to do this.

An alternative procedure for finding a one-way solution is based on the following theorem:

을 단-방향 전달 함수라고 하며 I를 H(ω)의 비 제로들을 포함한 주파수들(ω)의 개방 간격이라고 하자. 그 후 H(ω)의 위상 함수(α(ω))는 I에 대해 비연속적이어야 한다.Theorem 2:

Is called a one-way transfer function, and let I be the open interval of frequencies (ω) including nonzero H (ω). The phase function? (?) Of H (?) Should then be non-continuous with respect to I.

임이 이해된다. ε>0이 임의로 선택되었기 때문에, 우리는 ω→ω₀으로서 α(ω)→α(ω₀)임을 결론 내리며, 따라서 위상 함수(α(ω))는 ω₀에서 연속적이다. Proof : Let ω _{0 be} the frequency at interval (I). In Fig. 7, the complex value H (omega ₀ ) is plotted as a point in the complex plane, and, by hypothesis, H (? ₀ )? Let 竜 &gt; 0 be any arbitrary small real number and refer to two angles of the measured value ε shown in FIG. 7 as well as a circle centered on H (ω ₀ ) and tangent to two lasers (OA and OB) Consider it. By assumption, H (ω) is continuous for all ω. Thus, if ω is close enough to ω ₀ , the complex value H (ω) will be in the circle,

. Since ε> 0 is arbitrarily chosen, we conclude that ω → ω ₀ is α (ω) → α (ω ₀ ), so the phase function α (ω) is continuous at ω ₀ .

)의 비 제로들을 포함한 주파수들(ω)의 개방 간격이라고 하자.

를 β(ω)=2γ(ω) 및 γ(ω)가 I에 대해 연속적인 I에 대한 함수라고 하자. 그 후, J(ω) 및 -J(ω)는 I에 대한 G(ω)를 생성하는 단-방향 전달 함수들이며, 다른 것들은 없다.Theorem 3: I is a positive-directional transfer function (

) Is the open interval of frequencies (ω) including nonzero frequencies.

Let β (ω) = 2γ (ω) and γ (ω) be functions of I for successive I. Then, J (omega) and -J (omega) are the one-way transfer functions that produce G (omega) for I, and others do not.

)임을 알고 있으며, 여기에서 β(ω)=2α(ω). I에 대해 G(ω)≠0이므로, H(ω) 및 J(ω)는 I에 대해 비제로이다. 그 후, Proof : Proof is similar to Proof of Proof 1. We show that one of the solutions to the one-way transfer function is the function

), Where β (ω) = 2α (ω). H (?) And J (?) Are nonzero for I, since G (?)? 0 for I. After that,

(35)

(35)

By hypothesis, γ (ω) is continuous with respect to I, and by theorem 2 α (ω) is also continuous with respect to I. Therefore,? (?) -? (?) Is continuous with respect to I. The conditions β (ω) = 2α (ω) = 2γ (ω) mean that for each ωI, α (ω) - γ (ω) is 0 or π. However,? (?) -? (?) Does not become non-continuous with respect to I and can not switch between these two values. Therefore, for all ω ∈ I, α (ω) - γ (ω) = 0 or for all ω ∈ I α (ω) - γ (ω) = π. In the first case, we get J (ω) = H (ω), and in the second, J (ω) = - H (ω).

)의 비 제로들을 포함한 임의의 개방 간격(I)에 대한 단-방향 해법을 얻기 위해, 우리는 간단히 함수(

)를 형성하여, 위상 함수(γ(ω))를 연속적이게 하도록 하는 방식으로 β(ω)=2γ(ω)를 만족시키는 γ(ω)의 값들을 선택한다고 말한다. 이러한 속성, 즉 H(ω)을 가진 해법이 있다는 것이 알려져 있으므로, 이를 행하는 것이 항상 가능하다.Theorem 3 is the transfer function (

To obtain a one-way solution for any open interval (I), including nonzero, we simply use the function

) Is formed to select values of? (?) Satisfying? (?) = 2? (?) In such a manner as to make the phase function? (?) Continuous. It is known that there is a solution with this property, H (?), So it is always possible to do so.

These theorems show how to reconstruct the two short-direction transfer functions that produce the bi-directional function G (?), But they are only useful for the frequency interval I including the non-zero of G (?) Do. In general, G (?) Will be observed over a frequency interval (? ₁ ,? ₂ ) that may include zeros. The following can solve this problem assuming that there is only a finite number of zeros of G (?) At (? ₁ ,? ₂ ), and that the short-direction transfer function is all at a given frequency Is not a zero, but a derivative of all orders on (ω ₁ , ω ₂ ):

We assume that H (ω) is a short-direction function that generates G (ω) over intervals (ω ₁ , ω ₂ ) and G (ω) has at least one zero on (ω ₁ , ω ₂ ) . Zero G (ω) will be separated by (ω _1, ω ₂₎ of the adjacent open frequency interval of a finite number _{(J 1, J 2, ...} , J n). At each such interval, the solution H (ω) or -H (ω) will be found using theorem 1 or theorem 3. We need to "stitch together" these solutions so that the stitch solution is either H (ω) or -H (ω) over all of (ω ₁ , ω ₂ ). In order to do this, we use two adjacent sub-intervals (not shown) so that we do not switch from H (ω) to -H (ω) or from -H We need to know how to form a pair of solutions.

We illustrate a stitching procedure starting with the first two adjacent open sub-intervals (J ₁ and J ₂ ). These sub-intervals will be adjacent at a frequency (omega ₁ ) of G (?) (Of course, omega ₁ is not included in any one sub-interval). Due to our above assumption of the properties of the short-direction transfer function, there must be a minimum positive number (n) such that H ⁽ⁿ⁾ (ω ₁ ) ≠ 0, where the superscript (n) denotes the n-th derivative. Then as ω → ω ₁ from the left end in our J ₁ - limits of the n-th derivative of the direction of solution is based on whether our solution in J ₁ H (ω) or -H ^(ω) H ⁽ⁿ⁾ (? ₁ ) or -H ⁽ⁿ⁾ (? ₁ ). Similarly, the limit of the n-th derivative of our single-way solution at J ₂ as ω → ω ₁ from the right is H ⁽ⁿ ), depending on whether our solution at J ₂ is H (ω) or -H ⁾ (ω _¹⁾ or -H ^{(n) (ω ₁₎} it will be. Since H ⁽ⁿ⁾ (ω ₁ ) ≠ 0, the two limits will be the same only if the solutions at J ₁ and J ₂ are both H (ω), or both are -H (ω). If the left and right limits are not the same, we invert the solution at the sub-interval (J ₂ ). If not, we do not.

After taking the solution at subinterval J ₂ (if necessary), we perform the same procedure for subintervals J ₂ and J ₃ , and solve the solution at subinterval J ₃ (if necessary) Take off. Continuing this way, we eventually build a complete solution over the interval (ω ₁ , ω ₂ ).

It would be desirable that higher order derivatives of H (?) Are not required in the reconstruction procedure, since they are difficult to accurately calculate in the presence of noise. The problem is that at any zero of G (omega), it seems very likely that the first derivative of H (omega) is nonzero, or else it is very likely that the second derivative is nonzero, There is no possibility of occurrence.

In an actual technique, the bi-directional transfer function G (?) Will be measured at discrete frequencies, which must be close enough together to enable moderately accurate calculations of the derivatives near zero of G (?).

For RF-based distance measurements, it is necessary to resolve an unexpected number of closely spaced, overlapping, and noisy echoes of a distance measurement signal with a priori known shape. Assuming that the distance measurement signal is narrow-band, in the frequency domain, these RF phenomena may be described (modeled) as a sum of multiple sinusoids, each with multipath components and each having a complex attenuation and propagation delay of the path, .

Taking the above-mentioned sum Fourier transform will represent this multipath model in the time domain. When exchanging the role of time and frequency variables in this time domain representation, this multipath model will be a spectrum of harmonic signals whose path propagation delay is converted into a harmonic signal.

High resolution spectral estimation methods are designed to distinguish closely-located frequencies in the spectrum and are used to estimate individual frequencies of multiple harmonic signals, e.g., path delays. As a result, path delays can be accurately estimated.

The super resolution spectral estimation utilizes the eigen-structure and covariance matrix intrinsic properties of the covariance matrix of the base-band distance measurement signal samples to provide a solution to the fundamental estimate of the individual frequencies, e.g., path delays. One of the eigen-structure properties is that the eigenvalues can be combined and, as a result, can be divided into orthogonal noise and signal eigenvectors, also known as subspaces. Another inherent-structure property is the rotation-invariant signal subspace property.

Subspace decomposition techniques (MUSIC, rootMUSIC, ESPRIT, etc.) depend on decomposing the estimated covariance matrix of the observed data into two orthogonal subspaces, i.e., noise subspace and signal subspace. The theory of the subspace decomposition methodology is that the projection of the object to the noise subspace consists of only noise and that the projection of the object to the signal subspace consists only of the signal.

Spectral estimation methods assume that the signals are narrow-band and the number of harmonic signals is also known, i.e. the size of the signal subspace needs to be known. The size of the signal subspace is referred to as the model size. In general, it can not be known in any detail and can change rapidly as the environment changes - especially indoors. One of the most difficult and subtle issues when applying arbitrary subspace decomposition algorithms can be taken as the number of frequency components is present and is the dimension of the signal subspace which is the direct path plus the number of multipath reflections. Due to real-world measurement faults, there will always be errors in the model size estimation, which will result in frequency estimation, i.e., loss of accuracy of the distances.

To improve distance measurement accuracy, one embodiment includes six features that advance the state of the art in the methodology of subspace decomposition high resolution estimation. And combining two or more algorithms that estimate individual frequencies by using different eigen-structure properties that further reduce delay path determination ambiguity.

Root Music finds individual frequencies that minimize the energy of the projection when the object is projected into the noise subspace. The Esprit algorithm determines the individual frequencies from the rotation operator. At many points, this operation is a pair of Music in that it finds frequencies that maximize the energy of the projection when the object is projected onto the signal subspace.

The model size is the key for both of these algorithms, and indeed, in a complex signal environment as seen in the indoor distance measurement, the model size that provides the best performance for Music and Esprit is generally the reason .

For Music, it is desirable to make a mistake in terms of identifying the fundamental element of the decomposition as a "signal eigenvalue" (type I error). This will minimize the amount of signal energy projected onto the noise subspace and improve the accuracy. For Esprit - the opposite is true - it is desirable to make a mistake in terms of identifying the element of decomposition as a "noise eigenvalue". This again is a Type I error. This will minimize the effect of noise on the energy projected into the signal space. Therefore, the model size for Music will generally be somewhat larger than for Esprit.

Second, in complex signal environments, strong reflections and direct paths have virtually a much weaker probability than some of the multipath reflections, and model sizes have been found to have sufficient statistical reliability and difficult to estimate. This issue is addressed by processing the observed data using Music and Esprit in a window of model sizes defined by the base model size for each of the "basic" model sizes for both Music and Esprit. This results in multiple measurements for each measurement.

A first feature of the embodiment is the use of F-statistics to estimate the model size (see above). The second feature is the use of different Type I error probabilities in F-statistics for Music and Esprit. This implements Type I error differences between Music and Esprit as discussed above. The third feature is the use of the base model size and window to maximize the probability of detecting a direct path.

Because of the potentially rapidly changing physical and electronic environment, not all measurements will provide robust responses. This is handled by using cluster analysis for multiple measurements to provide robust range estimates. A fourth feature of the embodiment is the use of multiple measurements.

Because there are multiple signals present, the probability distribution of multiple responses due to multiple measurements, each using multiple model sizes from both Music and Esprit implementations, will be multimode. Conventional cluster analysis will not be sufficient for such applications. The fifth feature is the development of a multi-mode cluster analysis to estimate the direct range and equivalent range of the reflected multipath components. The sixth aspect is the analysis of the statistics (range and standard deviation and the combination of these estimates statistically identical) of the range estimates provided by the cluster analysis. This leads to more accurate range estimation.

The above-mentioned methods can also be used in wide bandwidth distance measurement signal position-finding systems.

For deriving r (t) in the threshold method, starting with equation (20), we obtain the following:

(A1)

(A1)

)이 사용된다.Here, the trigonometric equation (

) Is used.

Except for a ₀ , the coefficients a _k are 0 for an even k. The reason for this is that on the interval (I), the function (1 / sin [pi] ft) to be approximated by h (t) is even for the center of I, but the basic function for even k (Sin? Ft) are odd with respect to the center of I, and thus are orthogonal to I / sin? Thus, we can do substitution (k = 2n + 1) and let M be a positive odd number. In fact, we would say M = 2N + 1. This choice has been determined experimentally to provide a good positive erasure of vibrations at interval I.

(A2)

(A2)

Now we replace k = N-n in the first sum and k = N + n + 1 in the second sum to obtain:

(A3)

(A3)

Subtracting g (t) from s (t) causes:

(A4)

(A4)

Now let's do the next.

(A5)

(A5)

Thereafter, A4 can be recorded as follows.

(A6)

(A6)

These embodiments relate to wireless communication and a method of location / location in other wireless networks that substantially obviate one or more of the disadvantages of the related art. These embodiments advantageously improve the accuracy of tracking and location functions in multiple types of wireless networks by utilizing multi-path mitigation processes, techniques and algorithms described in U.S. Patent No. 7,872,583. These wireless networks include wireless personal area networks (WPGAN) such as ZigBee and Bluetooth, wireless local area networks (WLANs) such as WiFi and UWB, wireless metropolitan area networks (WMAN) typically comprised of multiple WLANs, , Wireless wide area networks (WAN) such as white space TV bands, and mobile device networks (MDN) typically used for transmitting voice and data. MDNs are typically based on Global System for Mobile Communications (GSM) and Personal Communications Services (PCS) standards. More recent MDNs are based on the Long Term Evolution (LTE) standard. These wireless networks typically include base stations, desktops, tablets and laptop computers, handsets, smart phones, actuators, dedicated tags, sensors, as well as other communication and data devices Network devices "). &Lt; / RTI &gt;

Conventional location and location information solutions use a number of technologies and networks, including GPS, AGPS, cell phone tower triangulation, and Wi-Fi. Some of the methods used to derive such location information include RF fingerprinting, RSSI, and TDOA. While acceptable for current E911 requirements, existing location and distance measurement methods have the reliability and accuracy required to support upcoming E911 requirements, as well as LBS and / or RTLS application requirements, particularly in indoor and urban environments Do not.

The methods described in U.S. Patent No. 7,872,583 considerably improve the ability to locate and track accurately targeted devices within a single wireless network or a combination of multiple wireless networks. Examples include tracking and location used by wireless networks using enhanced cell-ID and OTDOA (observed arrival time difference) including DL-OTDOA (downlink OTDOA), U-TDOA, UL-TDOA, It is a significant improvement over existing implementations of find methods.

The cell ID location description allows to estimate the location of the user (UE-user equipment) with the accuracy of a specific sector coverage area. Thus, the achievable accuracy depends on the cell (base station) sectoring scheme and the antenna beam-width. To improve accuracy, Enhanced Cell ID technology adds RTT (Round Trip Time) measurements from the eNB. Note: Here, RTT constitutes the difference between the transmission of the downlink DPCH-dedicated physical channel, (DPDCH) / DPCCH: dedicated physical data channel / dedicated physical control channel) frame and the beginning of the corresponding uplink physical frame . In this instance, the above-mentioned frame (s) operate as a distance measurement signal. Based on information on how long this signal propagates from the eNB to the UE, the distance from the eNB can be calculated (see Fig. 10).

In the observed arrival time difference (OTDOA) technique, the arrival time of signals from neighboring base stations (eNB) is calculated. The UE position can be estimated either in the handset (UE-based method) or in the network (NT-based, UE-assisted method) once the signals from the three base stations are received. The measured signal is CPICH (Common Pilot Channel). The propagation time of the signals correlates with locally generated replicas. The peak of the correlation represents the observation time of the propagation of the measured signal. The arrival time difference values between the two base stations determine the hyperbola. At least three reference points are required to define the two hyperbolas. The position of the UE is at the intersection of these two hyperbolas (see FIG. 11).

Idle Period Downlink (IPLD) is an additional OTDOA enhancement. The OTDOA-IPDL description is based on the same measurements that regular OTDOA time measurements are taken during idle periods, where the serving eNB stops its transmissions and allows the UE within the coverage of such a cell to access the remote eNB Of the pilots. The serving eNB provides idle periods in continuous or burst mode. In the continuous mode, one idle period is inserted in every downlink physical frame (10 ms). In burst mode, idle periods occur in a pseudo-random manner. Further improvements are obtained via time alignment IPDL (TA-IPDL). The time alignment creates a common idle period during which each base station will either stop its transmission or transmit a common pilot. The pilot signal measurements will occur during the idle period. There are several other technologies that can further enhance the DL OTDOA-IPDL method, such as cumulative virtual blanking, UTDOA (uplink TDOA), and the like. All these techniques improve the ability to listen to other (non-serving) eNB (s).

One significant disadvantage of OTDOA-based technologies is that, for this method to be feasible, base station timing relationships must be known or measured (synchronized). For unsynchronized UMTS networks, the 3GPP standard provides suggestions on how such timing can be recovered. However, network operators do not implement this solution. As a result, alternatives to using RTT measurements instead of CPICH signal measurements are proposed (see U.S. Patent Publication No. 20080285505, John Carlson et al., Systems and Methods for Network Timing Recovery in Communication Networks).

All the above-mentioned methods / techniques are based on terrestrial signal arrival time and / or arrival time difference measurements (RTT, CPICH, etc.). The issue with these measurements is that they are severely affected by the multi-path. This results in a significant reduction in the accuracy of locating / tracking the above mentioned methods / techniques (see Jakub Marek Borkowski: Performance of Cell ID + RTT Hybrid Positioning Method for UMTS).

One multi-path mitigation technique employs detections / measurements from an excessive number of eNB (s) or radio base stations (RBS). The minimum value is 3, but for multipath mitigation the number of RBS required is at least 6 to 8 (WO / 2010/104436, DL-OTDOA (downlink observation arrival time difference) in LTE (Long Term Evolution) Methods and apparatus for positioning). However, the probability of the UE listening from these multiple eNB (s) is much lower than from the three eNB (s). This may be because there are multiple RBSs (eNBs), there will be several more away from the UE and the signal received from these RBS (s) may fall below the UE receiver sensitivity level or the received signal will have a low SNR to be.

In the case of RF reflections (e.g., multi-path), multiple copies of the RF signal with various delay times are superimposed on the DLOS (direct high-line) signal. Because the CPICH, the uplink DPCCH / DPDCH, and other signals used in various cell ID and OTDOA methods / techniques, including RTT measurements, are bandwidth limited, the DLOS signal and the reflected signals may be subjected to appropriate multi-path processing / Can not be distinguished without; Without such multi-path processing, these reflected signals will include errors in the estimated arrival time difference (TDOA) and arrival time (TOA) measurements, including RTT measurements.

For example, the 3G TS 25.525 v.3.0.0 (199-10) standards define the RTT as the "transmission of the downlink DPCH frame (signal) and the start of the corresponding uplink DPCCH / DPDCH frame (signal) The first significant path). " The standard does not define what constitutes such a "first critical path &quot;. The standard begins to notice that "the definition of the first critical path requires further refinement &quot;. For example, in many multipath environments, it is common for a DLOS signal, which is the first significant path thereby, to be relatively heavily attenuated (10 dB to 20 dB) for one or more of the reflected signal (s). If the "first significant path" is determined by measuring the signal strength, it is one of the reflected signal (s) and may not be a DLOS signal. This will result in the loss of incorrect TOA / DTOA / RTT measurement (s) and location accuracy.

In previous generations of wireless networks, the positioning accuracy was also affected by the low sampling rate of the frames (signals) used by the location methods - RTT, CPCIH and other signals. Currently third and subsequent wireless network generations have a much higher sampling rate. As a result, in these networks, the actual accuracy of the latitude comes from terrestrial RF propagation phenomena (multipath).

Embodiments may be used in all wireless networks that utilize reference and / or pilot signals, and / or synchronization signals, including unidirectional, semi-dual and full-duplex modes of operation. For example, embodiments may operate with wireless networks that utilize OFDM modulation and / or its derivatives. Thus, the embodiment operates in conjunction with LTE networks.

It is also applicable to other wireless networks, including WiMax, WiFi and white space. Other wireless networks that do not use reference and / or pilot or synchronization signals may use one or more of the following alternative modulation embodiments, as described in U.S. Patent No. 7,872,583: 1) Portion is dedicated to distance measurement signal / distance measurement signal elements as described in U.S. Patent No. 7,872,583; 2) when the distance measurement signal elements (U.S. Patent No. 7,872,583) are embedded in the transmit / receive signals frame (s); And 3) the distance measurement signal elements (described in U.S. Patent No. 7,872,583) are embedded with the data.

These alternative embodiments utilize the multi-path mitigation processor and multi-path mitigation techniques / algorithms described in U.S. Patent No. 7,872,583 and can be used in all modes of operation: unidirectional, half-duplex and full duplex .

It is also possible that multiple wireless networks will utilize simultaneously, preferred and / or alternative embodiments. By way of example, a smartphone may have Bluetooth, WiFi, GSM and LTE capabilities with the ability to operate on multiple networks simultaneously. Depending on application requirements and / or network availability, different wireless networks may be used to provide location / location information.

The proposed embodiment method and system leverages wireless network reference / pilot and / or synchronization signals. Moreover, the reference / pilot signal / synchronization signal measurements can be combined with RTT (round trip time) measurements or system timing. According to an embodiment, RF-based tracking and location is implemented on 3GPP LTE cellular networks, but may be implemented on other wireless networks, such as WiMax, Wi-Fi, LTE, sensor networks, etc. using various signaling techniques It can also be implemented. Both representative and above-mentioned alternative embodiments utilize the multi-path mitigation methods / techniques and algorithms described in U.S. Patent No. 7,872,583. The proposed system can use software implemented digital signal processing.

The system of the embodiment leverages user equipment (UE), such as a cell phone or smart phone, hardware / software, as well as base station (Node B) / enhanced base station (eNB) hardware / software. A base station typically consists of transmitters and receivers in a cabin or cabinet connected to antennas by feeders. These base stations include microcells, picocells, macrocells, Umbrella cells, cell phone towers, routers, and femtocells. As a result, there will be little or no incremental cost for the UE device and the overall system. At the same time, the accuracy of locating will be significantly improved.

Improved accuracy comes from the multi-path mitigation provided by these embodiments and U.S. Patent No. 7,872,583. Embodiments use multi-path mitigation algorithms, network reference / pilot and / or synchronization signals and network nodes (eNB). These can be supplemented by RTT (Round Trip Time) measurements. The multi-path mitigation algorithms are implemented in the UE and / or base station (eNB), or both: UE and eNB.

Embodiments advantageously include multi-path mitigation processors / algorithms that allow for separating the DLOS signal and the reflected signals even when the DLOS signal is relatively relatively attenuated (from 10 dB to 20 dB or less) for one or more reflected signals (See U.S. Patent No. 7,872,583). Thus, embodiments reduce the error in the estimated distance measurement signal DLOS flight time and consequently TOA, RTT and DTOA measurements. The proposed multi-path mitigation and DLOS discrimination (recognition) method can be used on all RF bands and wireless systems / networks. It can support a variety of modulation / demodulation techniques, including spread spectrum techniques, such as DSS (Direct Spread Spectrum) and FH (Frequency Hopping).

Additionally, noise reduction methods can be applied to further improve the accuracy of the method. These noise reduction methods may include, but are not limited to, coherent summation, non-coherent summation, matched filtering, time diversity techniques, and the like. The remainder of the multi-path interference error may be further reduced by applying post-processing techniques such as maximum likelihood estimation (e.g., Viterbi algorithm), minimum variance estimation (Kalman filter), and the like.

In these embodiments, the multi-path mitigation processor and multi-path mitigation techniques / algorithms do not change RTT, CPCIH, and other signals and / or frames. These embodiments leverage the radio network reference, pilot and / or synchronization signals used to obtain the channel response / estimation. The present invention uses channel estimation statistics generated by a UE and / or an eNB (see Iwamatsu et al., Apparatus for Estimating Propagation Path Characteristics, US 2003/008156, US 7167456 B2).

LTE networks use specific (non-data) reference / pilot and / or synchronization signals (known signals) transmitted in all downlink and uplink subframes and can span the entire cell bandwidth. From now on, for simplicity, we will refer to the reference / pilot and synchronization signals as reference signals. An example of LTE reference signals is shown in Figure 9 (these signals are interspersed among LTE resource elements). From Fig. 2, reference signals (symbols) are transmitted every sixth subcarrier. In addition, reference signals (symbols) are staggered in both time and frequency. As a whole, the reference signals cover every third subcarrier.

These reference signals are used in the initial cell search by the UE, downlink signal strength measurements, scheduling and handover. UE-specific reference signals for channel estimation (response determination) for coherent demodulation are included in the reference signals. In addition to UE-specific reference signals, other reference signals may also be used for channel estimation purposes (see Chen et al., U.S. Patent Publication No. 2010/0091826 A1).

LTE uses OFDM (orthogonal frequency division multiplexing) modulation (technology). In LTE, inter-symbol interference (ISI) caused by multipath is handled by inserting a cyclic prefix (CP) at the beginning of each OFDM symbol. The CP provides sufficient delay for the delayed reflections of the previous OFDM symbol to disappear before it reaches the next OFDM symbol.

The OFDM symbol consists of a number of very closely spaced subcarriers. Within the OFDM symbol, time-staggered copies of the current symbol (caused by multipath) cause inter-carrier interference (ICI). In LTE, ICI is handled (mitigated) by determining the multipath channel response and correcting the channel response at the receiver.

In LTE, a multipath channel response (estimate) is calculated from subcarriers bearing reference symbols at the receiver. The interpolation is used to estimate the channel response for the remaining subcarriers. The channel response is calculated (estimated) in the form of channel amplitude and phase. Once the channel response is determined (by periodic transmission of known reference signals), channel distortion caused by multipath is mitigated by applying amplitude and phase shifts on a per subcarrier basis (White Paper, Jim Zyren, 3GPP Long Term Evolution Physics Overview of hierarchies).

LTE multipath mitigation is designed to remove the ISI (by inserting the cyclic prefix) and ICI, but not the DLOS signal from the reflected signals. For example, time-staggered copies of the current symbol cause each modulated sub-carrier signal to spread in time, thus causing ICI. Calibrating the multipath channel response using the above-mentioned LTE technique will retract the time-modulated subcarrier signals, but this type of calibration is advantageous because the resulting modulated subcarrier signals (within the OFDM symbol) Is not guaranteed. If the DLOS modulated subcarrier signals are relatively significantly attenuated for the delayed reflected signal (s), the resulting output signal will be delayed reflected signal (s) and the DLOS signal will be lost.

In an LTE compliant receiver, the additional signal processing includes a DFT (Digital Fourier Transform). It is well known that the DFT technique (s) can resolve (remove) only copies of the signal (s) that are delayed for times longer than or inversely proportional to the signal and / or channel bandwidth. While this method accuracy may be adequate for efficient data transmission, it is not accurate enough for accurate distance measurements in many multipath environments. For example, to achieve 30 meters accuracy, the signal and receiver channel bandwidths must be greater than or equal to 10 megahertz (1/10 MHz = 100 ns). For better accuracy, the signal and receiver channel bandwidths should be wider - 100 megahertz for 3 meters.

However, other signals used in various cell IDs and OTDOA methods / techniques, including CPICH, uplink DPCCH / DPDCH and RTT measurements, as well as LTE received signal subcarriers, have bandwidths significantly lower than 10 megahertz I have. As a result, currently used (in LTE) methods / techniques will generate positioning errors in the range of 100 meters.

In order to overcome the above-mentioned limitations, embodiments use a unique combination of implementations of subspace decomposition high resolution spectral estimation methods and multimode cluster analysis. Such analysis and associated multi-path mitigation methods / techniques and algorithms described in U.S. Patent No. 7,872,583 allow reliable and accurate separation of DLOS paths from other reflections signals paths.

These methods / techniques and algorithms (U. S. Patent No. 7,872, 583), in many multipath environments, compared to the methods / techniques used in LTE, are based on the trust of the DLOS path from other multipath (MP) Deliver accurate 20X to 50X accuracy improvements in distance measurements with accurate and accurate separation.

The methods / techniques and algorithms described in U.S. Patent No. 7,872,583 require a distance measurement signal complex amplitude estimation. Thus, LTE reference signals used for channel estimation (determination of a response) as well as other reference signals (including pilot and / or synchronization signals) may also be used with the methods / techniques and algorithms described in U.S. Patent No. 7,872,583 Can be interpreted as a distance measurement signal at In this case, the distance measurement signal complex amplitude is the channel response that is calculated (estimated) by the LTE receiver in the form of amplitude and phase. In other words, the channel response statistics calculated (estimated) by the LTE receiver may provide the complex amplitude information required by the methods / techniques and algorithms described in U.S. Patent No. 7,872,583.

In an ideal open space RF propagation environment without any multipath, the phase change of the received signal (distance measurement signal), for example the channel response phase, will be directly proportional to the frequency of the signal (straight line); In such an environment, the RF signal flight time (propagation delay) can be calculated directly from the phase versus frequency dependence by calculating the first derivative of the phase versus frequency dependence. The result will be a propagation delay constant.

In this ideal environment, the absolute phase value at the initial (or arbitrary) frequency is not important because the derivative is not affected by the phase absolute values.

In many multipath environments, the received signal phase shift versus frequency is a complex curve (not a straight line); The first derivative does not provide information that can be used for accurate separation of the DLOS path from other reflections signals paths. This is the reason for using the multipath mitigation processor and method (s) / techniques and algorithms described in U.S. Patent No. 7,872,583.

If the achieved phase and frequency synchronization (phase coherence) in a given wireless network / system is very good, the multipath mitigation processor and method (s) / techniques and algorithms described in U. S. Patent No. 7,872, Will accurately separate the DLOS path from the paths and determine this DLOS path length (flight time).

In such a phase coherent network / system, no additional measurements are required. In other words, one-way distance measurement (unidirectional distance measurement) can be realized.

However, if the degree of synchronization achieved in a given wireless network / system (phase coherence) is not sufficiently accurate, then in many multipath environments, the received signal phase and amplitude change versus frequency may be performed at two or more different locations (distances) It can be very similar to the measurements. This phenomenon can lead to ambiguity in the received signal DLOS distance (flight time) determination.

To solve this ambiguity, it is necessary to know the actual (absolute) phase value for at least one frequency.

However, the amplitude and phase to frequency dependence computed by the LTE receiver does not include the actual phase value since all amplitude and phase values are calculated from the downlink / uplink reference signals, e.g., for each other. Thus, the amplitude and phase of the channel response computed (estimated) by the LTE receiver requires an actual phase value at at least one frequency (subcarrier frequency).

In LTE, these actual phase values are: 1) these time stamps transmitting these signals by the eNB are also known at the receiver (and vice versa); 2) when the receiver and eNB clocks are well synchronized in time, And / or 3) from one or more of the RTT measurement (s), TOA measurements, using a multivariate measurement technique; Or from time-stamping of one or more received reference signals.

All of the above methods provide flight time values of one or more reference signals. From the flight time values and frequencies of these reference signals, actual phase values at one or more frequencies can be calculated.

These embodiments may be implemented in a multi-path mitigation processor, method (s) / algorithms, and algorithms described in U.S. Patent No. 7,872,583, including 1) amplitude and phase to frequency dependency computed by an LTE UE and / 2) a combination of amplitude and phase versus frequency dependence calculated by the LTE UE and / or eNB receiver and actual phase value (s) for one or more frequencies obtained via RTT and / or TOA; 0.0 &gt; and / or &lt; / RTI &gt; time-stamping measurements to achieve very accurate DLOS distance determination / location in many multipath environments.

In these cases, the actual phase value (s) are affected by multipath. However, this does not affect the performance of the methods / techniques and algorithms described in U.S. Patent No. 7,872,583.

In LTE RTT / TOA / TDOA / OTDOA, including DL-OTDOA, U-TDOA, and UL-TDOA, measurements can be performed with a resolution of 5 meters. RTT measurements are carried during dedicated connections. Thus, multiple simultaneous measurements are possible when the UE is in a handover state and the UE periodically collects measurements and reports it back to the UE, where the DPCH frames are exchanged between the UE and different networks (base stations) do. Similar to RTT, TOA measurements provide the flight time (propagation delay) of the signal, but TOA measurements can not be made simultaneously (Jakub Marek Borkowski: Performance of cell ID + RTT hybrid positioning method for UMTS).

To find the location of the UE on the plane, the DLOS distances should be determined for / from at least three eNBs. In order to find the location of the UE in the three-dimensional space, at least four DLOS distances should be determined from / to the four eNBs (assuming that at least one eNB is not on the same plane).

An example of a UE location method is shown in FIG.

In the case of very good synchronization, RTT measurements are not required.

If the degree of synchronization is not sufficiently accurate, methods such as OTDOA, Cell ID + RTT and others, such as AOA (Arrival Angle), and combinations thereof with other methods, may be used for UE location.

Cell ID + RTT tracking - Latitude method accuracy is affected by multipath (RTT measurement) and eNB (base station) antenna beamwidth. Base station antenna beam widths are between 33 and 65 degrees. These wide beam widths cause positioning errors of 50 to 150 meters in urban areas (Jakub Marek Borkowski: Performance of Cell ID + RTT Hybrid Locating Method for UMTS). In many multipath environments, considering the current LTE RTT distance measurement average error is approximately 100 meters, the overall expected average location error currently used by the LTE cell ID + RTT method is approximately 150 meters.

One of the embodiments is UE location based on the AOA method, whereby one or more reference signals from the UE are used for UE location purposes. It involves an AOA decision device location to determine the DLOS AOA. The device may be located at the same location as the base station and / or at another location or locations independent of the base station location. The coordinates of these locations are perhaps known. No changes are required on the UE side.

Such a device is based on a modification of the same multipath mitigation processor, method (s) / techniques and algorithms described in U.S. Patent No. 7,872,583, which includes a small antenna array. One such possible embodiment has the advantage of an accurate determination of the AOA of the DLOS RF energy (very narrow bandwidth) from the UE unit.

In one other option, this added device only receives the device. As a result, its size / weight and cost are very low.

Embodiments in which accurate DLOS distance measurements are obtained and combinations of embodiments in which an accurate DLOS AOA decision can be made will greatly improve the accuracy of the cell ID + RTT tracking-location method - above 10X. Another advantage of this approach is that the UE location can be determined at any time with a single tower (does not require placing the UE in soft handover mode). Since exact location fixes can be obtained with a single tower, there is no need to synchronize multiple cell towers. Another option for determining the DLOS AOA is to use existing eNB antenna arrays and eNB equipment. This option can further reduce the cost of implementing the enhanced cell ID + RTT mode. However, because the eNB antennas are not designed for locating applications, the positioning accuracy may be degraded. In addition, network operators may be reluctant to implement the required changes at the base station (software / hardware).

In the LTE (Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation; 3GPP TS 36.211 Release 9 Technical Specification), positioning reference signals (PRS) have been added. These signals will be used by the UE for DL-OTDA (downlink OTDOA) positioning. This Release 9 also requires that the eNB (s) be synchronized. Therefore, the last obstacle to OTDOA methods should be eliminated (see paragraph 274 above). The PRS improves UE audibility in UEs of multiple eNBs. Note: Release 9 does not specify eNB synchronization accuracy (some suggestions: 100 ns).

U-TDOA / UL-TDOA is in the research phase; It will be standardized in 2011.

The DL-OTDOA method (in Release 9) is described in detail in US Patent US 2011/0124347 A1 (Chen et al., Methods and Apparatus for UE Positioning in LTE Networks). Release 9 DL-OTDOA undergoes multipathing. Some of the multipath mitigation can be achieved through increased PRS signal bandwidth. However, the trade-off is increased scheduling complexity and longer times between UE location fixes. In addition, for networks with limited operating bandwidth, for example 10 MHz, the best possible accuracy is 100 meters, see Table 1 of Chen.

These numbers are the best possible cases. Other cases result in the above-mentioned positioning / distance measurement errors which are significantly greater (2X to 4X), especially when the DLOS signal strength is significantly lower (10-20 dB) compared to the reflected signal (s) strength.

The embodiments described herein permit an improved distance measurement / positioning accuracy improvement of up to 50X for a given signal bandwidth compared to the performance achieved by the UL-PRS method other than Chen described in the background section and the Release 9 DL-OTDOA method . Accordingly, applying embodiments of the methods described herein to Release 9 PRS processing reduces positioning errors to less than 3 meters down from 95% of all possible cases. This accuracy gain will also reduce the scheduling complexity and time between UE position fixes.

With the embodiments described herein, further improvements to the OTDOA method are possible. For example, a ranging measurement for a serving cell may be determined from signals of other serving cells, thus improving neighboring cells &apos; visibility and reducing scheduling complexity, including time between UE position fixes.

Embodiments allow the accuracy of the U-TDOA method (described in the background) and UL-TDOA to be improved up to 50 times from Chen et al. Applying the embodiments to Chen's UL-TDOA variant reduces the positioning error to less than 3 meters below 95% of all possible cases. In addition, this accuracy gain further reduces the scheduling complexity and time between UE position fixes.

Again, with these embodiments, Chen's UL-TDOA method accuracy can be improved to 50X. Therefore, applying these embodiments to Chen's U-TDOA variant will reduce the positioning error to less than 3 meters below 95% of all possible cases. In addition, this accuracy gain will further reduce the scheduling complexity and time between UE position fixes.

The above-mentioned DL-TDOA and U-TDOA / UL-TDOA methods rely on single-direction measurements (distance measurement). The present embodiments and substantially all other distance measurement techniques require that the PRS and / or other signals used in the process of single-direction distance measurement be frequency and phase coherent. Like LTE, OFDM-based systems are frequency coherent. However, the UE units and the eNB (s) are not phase or time synchronized by a common source like UTC to several nanoseconds, for example, there is a random phase adder.

To avoid phase coherence effects on distance measurement accuracy, an embodiment of a multipath processor may include a differential phase between the distance measurement signal (s), e.g., reference signals, individual components (subcarriers) . This removes the random phase period adder.

Applying the embodiments described herein, as identified above in the discussion of Chen et al., Causes significant accuracy improvements in indoor environments compared to the performance achieved by Chen et al. For example, according to Chen et al., DL-OTDOA and / or U-TDOA / UL-TDOA are mostly for outdoor environments, indoors (buildings, campuses, Can not be performed. Several reasons are mentioned, including distributed antenna systems (DAS), which are typically used indoors (Chen, # 161-164), whereby each antenna does not have a unique ID.

The embodiments described below include wireless networks using OFDM modulation and / or its derivatives; And reference / pilot and / or synchronization signals. Thus, the embodiments described below operate with LTE networks and it is also possible to use other wireless systems and other wireless networks, including other types of modulation, with or without reference / pilot / and / Lt; / RTI &gt;

The approach described here is also applicable to other wireless networks, including WiMax, WiFi and white space. Other wireless networks that do not use reference / pilot and / or synchronization signals may use one or more of the following alternative modulation embodiments, as described in U.S. Patent No. 7,872,583: 1) When a portion is dedicated to distance measurement signal / distance measurement signal elements; 2) when the distance measurement signal elements are embedded in the transmit / receive signals frame (s); And 3) the distance measurement signal elements are embedded with the data.

Embodiments of the multipath mitigation range estimation algorithm described herein (and also described in U.S. Patent Nos. 7,969,311 and 8,305,215) are estimates of the estimates of the ranges of multipath reflections in the aggregate consisting of multipath reflections plus the direct path of the signal (DLOS) Lt; / RTI &gt;

The LTE DAS system generates multiple copies of the same signal shown at various time offsets for the mobile receiver (UE). The delays are used to uniquely determine the geometric relationships between the antennas and the mobile receiver. The signal shown by the receiver is similar to that shown in the multipath environment, except that the main "multipath" components are due to the sum of the offset signals from the multiple DAS antennas.

The signal aggregate shown by the receiver is identical to the type of signal aggregate that the embodiments are designed to use, except that in this case, the major multipath components are not conventional multipath. The current multipath mitigation processors (algorithms) can determine the DLOS and the attenuation and propagation delay of each path, e. G., Reflections (see Equations 1 through 3 and related explanations). Multipaths may exist because of distributed RF channels (environments), but the major multipath components in such a signal aggregate are associated with transmissions from multiple antennas. Embodiments of the present multipath algorithm may estimate these multipath components, separate ranges of DAS antennas for the receiver, and provide range data to a location processor (implemented in software). Depending on the antenna placement geometry, this solution may provide both X, Y and X, Y, Z position coordinates.

As a result, the embodiments do not require any hardware and / or new network signal (s) additions. In addition, the positioning accuracy can be reduced by 1) mitigating multipath, and 2) in the case of active DAS, the lower limit of the positioning error can be dramatically reduced, such as from about 50 meters to about 3 meters Can be significantly improved.

It is assumed that the location (location) of each antenna of the DAS is known. The signal propagation delay of each antenna (or for the other antenna) should also be determined (it should be known).

For active DAS systems, the signal propagation delay can be determined automatically using loopback techniques, whereby a signal known by it is transmitted in a round trip and this round trip time is measured. This loopback technique also eliminates signal propagation delay variations (drift) due to temperature, time, and the like.

Using multiple macrocells and associated antennas, the picocells and microcells further enhance resolution by providing additional reference points.

The described embodiment of the individual range estimates from the signal aggregate of multiple copies from multiple antennas can be further enhanced with respect to changes to the signal transmission structure in the following two ways. The first is to time multiplex transmissions from each antenna. The second approach is to frequency multiplex for each of the antennas. Using both enhancements, simultaneous time and frequency multiplexing further improves the distance measurement and position accuracy of the system. Another approach is to add a propagation delay to each antenna. The delay values will be selected to be large enough to exceed the delay spread in a particular DAS environment (channel), but the multipath caused by the additional delays is less than the cyclic prefix (CP) length so as not to cause ISI It will be small.

The addition of a unique ID or unique identifier for each antenna increases the efficiency of the resulting solution. For example, it eliminates the need for a processor to remove all of the ranges from the signals from each of the antennas.

In one embodiment using an LTE downlink, one or more reference signal (s) subcarriers, including pilot and / or synchronization signal (s) subcarriers, may result in using multisurvey and locality coherence algorithms to truncate the wild points Are used to determine the phase and amplitude of the subcarriers applied to the multi-path processor for generation of range-based position objects and location estimates and mitigation of multipath interference.

Another embodiment utilizes the fact that LTE uplink signaling also includes reference signals, and also mobile devices for the base including reference subcarriers. In fact, from the total sounding mode used by the network to allocate the frequency band to the uplink device, the reference subcarriers are used to generate these subcarriers up to the mode used to generate the channel impulse response to aid demodulation, There is one or more modes to include. Also, similar to the DL PRS added to Release 9, additional UL reference signals may be added in upcoming and future standard releases. In this embodiment, the uplink signal is processed by multiple base units (eNBs) using the same range for phase, multipath mitigation processing to generate range related observations. In this embodiment, location consistency algorithms are used as edited by the multivariate survey algorithm to edit the wild point observed objects and generate the position estimate.

In another embodiment, when one or more associated subcarriers (including pilot and / or synchronization) of both LTE downlink and LTE uplinks are collected, range-to-phase mapping is applied, multipath mitigation is applied, Associated objects are estimated. These data will then be fused in such a way as to provide a more robust set of observations to the location using multivariate and locality consistency algorithms. This will be a redundancy that leads to improved accuracy either in the case of TDD (time division duplexing) that improves system coherence or downlink frequency uplinks and two different frequency bands.

In a DAS (Distributed Antenna System) environment in which multiple antennas transmit the same downlink signal from a microcell, the location coherence algorithm (s) may include multipath mitigation processing from the reference signal (s) (pilot and / or synchronization) And to obtain position estimates from a plurality of DAS emitters (antennas) ranges.

In a DAS system (environment), obtaining an accurate position estimate is only possible if the signal paths from the individual antennas can be decomposed with high accuracy, whereby the path error is only a function of the distance between the antennas Part (accuracy over 10 meters). Since all existing techniques / methods can not provide this accuracy in many multipath environments (signals from multiple DAS antennas will appear like many derived multipaths), existing techniques / Algorithm (s) and the above-mentioned extensions of this location method / technique in a DAS environment.

The InvisiTrack multi-path mitigation methods and systems for object identification and location, as described in U.S. Patent No. 7,872,583, are based on LTE downlink, uplink and / or both (downlink and uplink) Multipath interference mitigation and processes to generate range-based positional observations using multivariate metrology and positional consistency to generate position estimates using the (s) subcarriers.

In all of the above embodiments, trilateration locating algorithms can also be used.

DL-OTDOA Latitude is specific to LTE Release 9: Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation; 3GPP TS 36.211 Release 9 Technical Specification. However, it was not implemented by wireless operators (carriers). In the meantime, the downlink location can be implemented in the current, e.g., unmodified, LTE network environment by using the existing physical layer measurement operation (s).

In LTE, the UE and the eNB are required to make physical layer measurements of radio characteristics. Measurement definitions are specified in 3GPP TS 36.214. These measurements are periodically performed and reported to higher layers and supported by inter-frequency and inter-frequency handover, inter-radio access technology (RAT-to-H) handover, timing measurements, and RRM It is used for various purposes including other purposes.

For example, RSRP (reference signal receive power) is an average of the power of all resource elements carrying cell-specific reference signals over the entire bandwidth.

Another example is a reference signal reception quality (RSRQ) measurement that provides additional information (RSRQ combines signal strength as well as interference level).

The LTE network provides eNB neighbor list (for serving eNB) to the UE. Based on the network knowledge configuration, the (serving) eNode B provides the UE with the identifiers of neighboring eNBs, and so on. The UE then measures the signal quality of the neighbors it can receive. The UE reports the results back to the eNodeB. Note: The UE also measures the signal quality of the serving eNB.

According to the specification, RSRP is defined as a linear average of power contributions (in [W]) of resource elements carrying cell-specific reference signals within the considered measurement frequency bandwidth. The measurement bandwidth used by the UE to determine the RSRP depends on the UE implementation with the limitation that the corresponding measurement accuracy requirements must be fulfilled.

Given the measurement bandwidth accuracy requirements, this bandwidth is very large and the cell-specific reference signals used for RSRP measurements are consequently those criteria that apply to multi-path processors for multipath interference mitigation and generation of range- The signals may be further processed to determine the phase and amplitude of the subcarriers. In addition, other reference signals, such as SSS (secondary synchronization signal), used for RSRP measurement may also be used.

The location fix can then be estimated using multivariate survey and location consistency algorithms based on range objects from three or more cells.

As mentioned previously, there are several causes of RF fingerprinting database instability, but one of the major ones is multipath (the RF signature is very sensitive to multipath). As a result, the RF fingerprinting method (s) / technique locating accuracy can be varied by multipath dynamics-over time, including > 100% variability depending on vertical uncertainty: device Z-height and / , Environment (e.g., weather), people and / or objects movement (see Tsung-Han Lin et al., Microscopy of RSSI-signature-based indoor localization systems).

These embodiments can significantly improve the RF fingerprinting locator accuracy because of the ability to discover and characterize each individual path, including a significantly attenuated DLOS (multipath processor). As a result, the RF fingerprinting decision for the position fix can be supplemented with real-time multipath distribution information.

As mentioned above, the position fix will require location-based synchronization in time. In wireless networks, these location criteria may include access points, macro / mini / pico and femtocell, as well as so-called small cells (eNB). However, wireless operators do not implement the synchronization accuracy required for correct location fixes. For example, in the case of LTE, the standard does not require any time synchronization between the eNB (s) for FDD (Frequency Division Duplex) networks. For LTE TDD (Time Division Duplex), this time synchronization accuracy has a limit of +/- 1.5 microseconds. This is like a 400+ meter positioning uncertainty. Although not required, LTE FDD networks are also synchronized, but use much larger (less than 1.5 microseconds) limits.

Wireless LTE operators are using GPS / GNSS signals to synchronize eNB (s) in frequency and time. Note: The LTE eNB should maintain a carrier frequency of 0.05 ppm for very accurate carrier frequencies for macro / mini-cells and slightly less accurate (0.1 to 0.25 ppm) for other types of cells. GPS / GNSS signals can also enable the required (for locating) time synchronization accuracy of more than 10 nanoseconds. However, network operators and network equipment manufacturers may support packet transport /, e.g., Internet / Ethernet networking time synchronization, by using NTP (Network Time Protocol) and / or PTP (Precision Time Protocol), for example IEEE 1588v2 PTP Try to reduce the costs associated with GPS / GNSS units.

IP network-based synchronization has the potential to meet minimum frequency and time requirements, but lacks the GPS / GNSS precision required for locating fixes.

The approach described herein is based on GPS / GNSS signals and signals generated by eNB and / or AP, or other wireless networks equipment. It may also be based on IP networking synchronization signals and protocols and signals generated by eNB and / or AP, or other wireless networks equipment. This approach is also applicable to other wireless networks, including WiMax, WiFi, and white space.

The eNB signals are received by a time observation unit (TMO) installed in the operator's eNB facility (Fig. 12). The TMO also includes an external synchronization source input.

The eNB signals are time stamped using clocks processed by the TMO and synchronized with an external synchronization source input.

The external synchronization source may come from GPS / GNSS and / or Internet / Ethernet networking, e.g. PTP or NTP.

The time-stamped processing signal, e.g., an LTE frame start (which may be other signals, especially in other networks), also includes an eNB (cell) location and / or cell ID, and transmitted to a central TMO server that creates, maintains, and updates a database of eNBs.

The UE and / or eNB (s) involved in the process of measuring the distance of the position fix and acquiring it will require the TMO server and the server will return the time synchronization offsets between the entitled eNB (s). These time synchronization offsets will be used by the UE and / or eNB (s) involved in the process of obtaining the position fix to adjust the position fix.

Alternatively, the position fix calculations and adjustments may be performed by the TMO server when the UE and / or eNB (s) involved in the distance measurement process also supply the obtained distance measurement information to the TMO server. The TMO server will then return the (adjusted) location (locating) fix.

If more than one cell eNB equipment is co-located, a single TMO can process and time stamp signals from all eNB (s).

RTT (round trip time) measurements (distance measurement) can be used for locating. The drawback is that the RTT distance measurement undergoes multipath with a drastic effect on the latitude accuracy.

On the other hand, RTT locating generally does not require eNB in particular in the case of location-based synchronization (in time) and LTE.

At the same time, the multi-path mitigation processor, method (s) / techniques and algorithms described in U.S. Patent No. 7,872,583, when operating with pilot reference and / or other signals of the wireless network, To identify the multi-path channel that the RTT signal (s) has undergone. This allows the RTT measurements to be calibrated so that the actual DLOS time is determined.

It would be possible to have a known DLOS time and obtain a position fix using trilateration and / or similar positioning methods without requiring the eNB or location-based synchronization in time.

Having an active TMO and TMO server, the technical integration of InvisiTrack will require changes in macro / mini / pico and small cells and / or UE (cell phone). These changes are limited to SW / FW (software / firmware), but it takes a lot of effort to retrofit existing infrastructure. Also, in some cases, network operators and / or UE / cell phone manufacturers / suppliers are opposed to equipment changes. Note: UE is a wireless network user equipment.

This SW / FW change can be completely avoided if the TMO and TMO server functions are extended to support InvisiTrack location-finding technology. In other words, another embodiment described below operates with wireless network signals, but does not require any modifications of the wireless network equipment / infrastructure. Thus, the embodiments described below operate with LTE networks and it is also applicable to other wireless systems / networks, including Wi-Fi.

In essence, this embodiment creates a parallel wireless location infrastructure using wireless network signals to obtain location fixes.

Similar to the TMO and TMO servers, the InvisiTrack's location infrastructure determines the range and locations, and converts it into a table of locations in the phone / UE IDs and time instance, (NSAUs) and one or more location server units (LSUs) that collect data from the NSAU (s) and analyze it. The LSU interfaces to the wireless network through an API of the network.

Many of these units can be deployed at various locations in a large infrastructure. If the NSAU (s) have coherent timing, the results for both can be used to provide better accuracy.

The coherent timing may be derived from a GPS clock and / or other stable clock sources.

The NSAU communicates with the LSU via a LAN (Local Area Network), an Urban Area Network (MAN) and / or the Internet.

In some installations / instances, the NSAU and LSU may be combined and / or integrated into a single unit.

In order to support location services using LTE or other wireless networks, transmitters are required to be clocked and event synchronized into tight tolerances. Usually this is accomplished by being fixed to one PPS signal of the GPS. This will cause timing synchronization in the localized region with 3 nanosecond 1-sigma.

However, there are many instances when this type of synchronization is not feasible. These embodiments provide tracking of time offsets and time offset estimates between downlink transmitters to provide delay compensation values to the location process so that the location process can proceed as if the transmitters were clocked and event synchronized. This is accomplished by having a known a priori antenna position and previous knowledge of the transmit antenna (required for any location services) and receiver. This receiver, called a synchronization unit, collects data from all downlink transmitters and calculates offset timing from the preselected base antenna, taking into account its knowledge of the locations. These offsets are tracked by the system through the use of a tracking algorithm that compensates for the clock drifts of the downlink transmitters. Note: Processing to derive pseudoranges from the received data will use InvisiTrack multipath mitigation algorithms (described in U.S. Patent No. 7,872,583). Therefore, synchronization will not be affected by multipath.

These offset data are used by a location processor (location server, LSU) to properly align data from each downlink transmitter so that it appears to have been generated by synchronized transmitters. Time accuracy is comparable to the best 1-PPS tracking and will support 3-meter position accuracy (1-sigma).

The antennas of the synchronization receiver and / or receiver will be located based on the optimal GDOP for best performance. In large installations, multiple synchronization receivers may be used to provide an equivalent 3 nanosecond 1-sigma synchronization offset across the network. By using the synchronization receiver (s), the requirements for synchronization of the downlink transmitter are eliminated.

The synchronization receiver unit may be a stand-alone unit that communicates with the NSAU and / or the LSU. Alternatively, this synchronization receiver may be integrated with the NSAU.

An exemplary wireless network location device diagram is depicted in FIG.

An embodiment of a fully autonomous system that uses LTE signals, without any customer network investment, operates in the following modes:

1. Uplink Mode - Uses wireless network uplink (UL) signals for the purpose of locating (Figures 16 and 17).

2. Downlink Mode-Uses wireless network downlink (DL) signals for the purpose of locating (Figures 14 and 15).

3. Bi-directional mode - both for locating: use UL and DL signals.

In the uplink mode, multiple antennas are connected to one or more NSAUs. These antenna positions are independent of the wireless network antennas; The NSAU (s) antenna positions are selected to minimize GDOP (reduction in geometric precision).

The RF signals of the network from the UE / cell phone devices are collected by the NSAU (s) antennas and are time stamped samples of the RF signals of the processed network for a time interval suitable for capturing one or more instances of all signals of interest And processed by the NSAU (s) to generate.

Optionally, the NSAU will receive, process, and time stamp samples of the downlink signals to determine additional information, e.g., UE / phone ID, and the like.

From the captured time stamped samples, the UE / cell phone devices identification numbers (ID) are determined (associated) with the time stamped wireless network signals of interest associated with each UE / cell phone ID (s) will be. This operation can be performed by the NSAU or by the LSU.

The NSAU will periodically supply data to the LSU. If unscheduled data is required for one or more UE / cell phone ID (s), the LSU will request additional data.

No changes / modifications will be required in the wireless network infrastructure and / or existing UE / cell phones for UL mode operation.

In the downlink (DL) mode, an InvisiTrack capable UE will be required. Also, the cell phone FW will have to be modified if the phone is used to obtain a location fix.

In some instances, operators may make baseband signals available from the BBU (s) (baseband units). In these cases, the NSAU (s) will also be able to process these available baseband wireless network signals instead of RF wireless network signals.

In DL mode, these signals are processed in a UE / cell phone or periodically generate time-stamped samples of RF signals of the network in which the UE / cell phone is processed and transmit them to the LSU; There is no need to associate the UE / cell phone ID with one or more wireless network signals because the LSU will send the result (s) back to the UE / cell phone.

In DL mode, the NSAU will process and time stamp the processed RF or baseband (when available) wireless network signals. From the captured time stamped samples, the beginning of the wireless network signal DL frames associated with the network antennas will be determined and a difference (offset) between these frame starts will be calculated. This operation can be performed by the NSAU or by the LSU. Frame start offsets for the network antennas will be stored on the LSU.

In DL mode, the frame start offsets of network antennas will be transmitted from the LSU to the UE / phone device when the device processes / resolves its own position fix using InvisiTrack technology. Otherwise, when the UE / cell phone device periodically transmits time stamped samples of the RF signals of the processed network to the LSU, the LSU will determine the device's location fix and send the location fix data back to the device.

In the DL mode, the wireless network RF signals will come from one or more wireless network antennas. To avoid multipath effects on the resulting accuracy, the RF signal must be found from the antenna or antenna connection to the wireless network equipment.

The bi-directional mode includes both: determination of the position fix from UL and DL operations. This allows further improvement of the latitude accuracy.

Some enterprise setups use one or more BBUs to provide one or more remote radio heads (RRH), each RRH serving a plurality of antennas with the same ID as a result. In these circumstances, depending on the wireless network configuration, it may not be required to determine the DL mode frame start offsets of the network antennas. This includes a number of BBUs as well as a single BBU setup, whereby the antennas of each BBU are assigned to a particular zone and adjacent zone coverage overlaps.

On the other hand, the configuration, whereby the antennas supplied by multiple BBUs are interleaved in the same zone, will require determining the DL mode frame starts offset of the network antennas.

In the DL mode of operation in a DAS environment, multiple antennas may share the same ID.

In these embodiments, the location consistency algorithm (s) separates the range of DAS antennas from the observations generated by multipath mitigation processing from the reference signal (s) (including pilot and / or synchronization) And to obtain position estimates from ranges of multiple DAS emitters (antennas).

However, these consistency algorithms have limitations on the number of antennas that emit the same ID. It is possible to reduce the number of antennas that emit the same ID by:

1. For a given coverage area, interleaves the antennas supplied from different sectors of the sectorized BBUs (BBUs can support up to six sectors)

2. For a given coverage area, the antennas supplied from the different sectors of the sectorized BBU as well as the antennas supplied from different BBUs are interleaved

3. Add propagation delay elements to each antenna. The delay values will be selected to be large enough to exceed the delay spread in a particular DAS environment (channel), but the cyclic prefix (CP) length (length) may be chosen so that multipaths caused by additional delays do not cause ISI Lt; / RTI &gt; The addition of the unique delay ID to one or more antennas further reduces the number of antennas that emit the same ID.

In an embodiment, an autonomous system may be provided that does not have any customer network investment. In such an embodiment, the system may operate on a band other than the LTE band. For example, ISM (industrial industrial and medical) bands and / or white space bands may be used where LTE services are not available.

Embodiments may also be integrated with macro / mini / pico / femto station (s) and / or UE (cell phone) equipment. Integration can require customer network investment, but it can reduce cost overhead and dramatically improve the total cost of ownership (TCO).

As noted above, the PRS may be used by the UE for downlink observation arrival time difference (DL-OTDOA) positioning. Regarding synchronization of neighboring base stations (eNBs), 3GPP TS 36.305 (Stage 2 functional specification of user equipment (UE) location in E-UTRAN) specifies delivering timing to the UE, E. G., Neighboring cells). 3GPP TS 36.305 also specifies physical cell IDs (PCIs) and global cell IDs (GCIs) of candidate cells for measurement purposes.

According to 3GPP TS 36.305, this information is communicated from the E-MLC (Enhanced Serving Mobile Location Center) server. It should be noted that TS 36.305 does not specify the timing accuracy mentioned above.

In addition, 3GPP TS 36.305 specifies that the UE will return with E-MLC downlink measurements, which includes reference signal time difference (RSTD) measurements.

The RSTD is a measurement taken between a pair of eNBs (TS 36.214 Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer Measurements; see Release 9). The measurement is defined as the relative timing difference between the subframe received from the neighboring cell j and the corresponding subframe of the serving cell i. Positioning reference signals are used to make these measurements. The results are reported back to the location server that computes the location.

In an embodiment, the hybrid method may be defined to accommodate both the newly introduced PRS and pre-existing reference signals. In other words, the hybrid method may have a PRS, have other reference signals (e.g., cell or node-specific reference signals (CRS)), or use / operate with both signal types.

This hybrid method provides the advantage of allowing the network operator (s) to dynamically select the mode of operation depending on situations or network parameters. For example, PRS has better audibility than CRS, but can cause a 7% reduction in data throughput. On the other hand, CRS signals do not cause any throughput reduction. In addition, the CRS signals are backwards compatible with all previous LTE releases, e. G., Release 8 and below. As such, the hybrid method provides the network operator with the ability to trade off or balance between audibility, throughput, and compatibility.

In Long Term Evolution (LTE) implementations, LTE downlink baseband signals (generated by a cell or wireless node, referred to herein as "nodes") are typically combined into downlink frames. A receiver for detecting and receiving such signals may detect downlink frames from a plurality of cells or nodes (two or more). Each downlink frame includes a plurality of CRSs or reference signals. In a downlink (DL) frame, these reference signals have predetermined positions in time and frequency, for example, there are deterministic time offsets between each CRS and frame start in a given frame.

Also, each CRS is modulated with a special code. The modulation and code are also predetermined. The CRS modulation is the same for all nodes, but the code (seed) is determined by the ID (identification) number of the node.

As a result, it is possible to estimate the course position of the frame start time for each frame from each node (cell) in the spectrum of the reference signals by knowing the node ID (s). To do so, it is first necessary to determine frame starts or frame start times for all DL signals from different nodes. For example, in an embodiment, by correlating received DL baseband signals with known replicas of a code modulated CRS (internally generated by a detector and / or multipath mitigation processor), all CRS sequences Or other reference signals, and with this information it is possible to find the course position frame starts of all observable nodes. In an embodiment, the detector may also demodulate / decode the CRS and then correlate the demodulated / decoded CRS with the baseband sub-carriers allocated to the CRS.

At the same time, in an embodiment, the CRS can also be used as distance measurement signals by a multipath mitigation processor. Therefore, in addition to finding the course frame starts, the correlation process of the detector can also separate the CRS from other signals (such as payload) in the frame using the code used to modulate these signals. These separate CRSs and associated frame starts are then passed to the multipath mitigation processor for distance measurement.

A similar approach can be used in the uplink mode, whereby the timing offsets between different node receivers can be determined.

In a downlink embodiment, a system for tracking and locating one or more wireless network devices in communication with a network is configured to receive a plurality of signals from two or more nodes in communication with a network, Signals are modulated with a code determined by an identification of a respective node of two or more nodes transmitting the plurality of signals, the user equipment receiver detecting reference signals based on the identification and separating the signals from the plurality of signals And a processor configured to use the reference signals as distance measurement signals from each of the nodes to track the one or more wireless network devices and locate its position.

In an embodiment, a plurality of signals from each node of the two or more nodes are combined into a frame comprising the reference signals, and the detector is also configured to estimate a course position of frame starts from each node.

In an embodiment, the detector is also configured to estimate the course position by correlating reference signals with known replicas of these reference signals.

In an embodiment, the detector is also configured to separate reference signals from any other signals in the frame, and the detector is also configured to separate reference signals for each node of the two or more nodes.

In an embodiment, the processor is at least one multipath mitigation processor, wherein the multipath mitigation processor is configured to receive the course locations and separate reference signals and to estimate the relative arrival times of the distance measurement signals from each node.

In an embodiment, the processor is at least one multipath mitigation processor.

In an embodiment, a plurality of signals from each node of two or more nodes are in a frame, wherein the detector is also configured to estimate the course position of frame starts from each node, and wherein the detector is configured to estimate Wherein the detector is further configured to isolate reference signals for each node of the two or more nodes, and wherein the detector is configured to transmit the course position for each node and separate reference signals to the multipath mitigation processor Wherein the multipath mitigation processor is configured to receive the course locations and the separate reference signals and to estimate the relative arrival times of the distance measurement signals from each of the nodes.

In an embodiment, the system further comprises an uplink embodiment in which the node receiver is configured to receive device signals from one or more wireless network devices, wherein the device signals are transmitted to each wireless network device Wherein the node receiver comprises a device detector configured to detect device reference signals based on the device identification and to separate the device reference signals from the device signals and the second processor includes a device detector configured to detect device reference signals based on the device identification, And is configured to use device reference signals as distance measurement signals from each wireless network device to track devices and locate its location.

In an embodiment, a system for tracking and locating one or more wireless network devices in communication with a network is configured to receive a plurality of signals from two or more nodes in communication with a network, Wherein the user equipment receiver is modulated with a code determined by an identification of a respective node of two or more nodes transmitting the plurality of signals; and detecting the reference signals based on the identification, separating the reference signals from the plurality of signals, And a processor configured to use the reference signals as distance measurement signals from each node to track one or more wireless network devices and locate its location.

In an embodiment, multiple signals from each node of two or more nodes are combined into a frame containing the reference signals, and the processor is also configured to estimate the course position of frame starts from each node.

In an embodiment, the processor is also configured to estimate the course position by correlating the reference signals with known replicas of the reference signals.

In an embodiment, the processor is also configured to estimate the relative arrival time of the distance measurement signals from each node based on the course position and the separated reference signals.

In an embodiment, the processor is further configured to separate the reference signals from any other signals in the frame, and the processor is also configured to separate reference signals for each node of the two or more nodes.

In an embodiment, multiple signals from each node of two or more nodes are in a frame, where the processor also estimates a course position of frame starts from each node by correlating reference signals with known replicas of the reference signals And the processor is further configured to separate reference signals from any other signals in the frame and to separate reference signals for each node of the two or more nodes, and wherein the processor is further configured to: And to estimate the relative arrival times of the distance measurement signals from each node based on the estimated arrival time.

In an embodiment, a system for tracking and locating one or more wireless network devices in communication with a network receives a plurality of signals from two or more nodes in communication with a network, the plurality of signals including a plurality of signals A detector configured to demodulate reference signals from the plurality of signals based on the receiving and the identification, wherein the detector is modulated with a code determined by an identification of a respective node of two or more transmitting nodes; And to use reference signals as distance measurement signals from each node to track and locate its position.

In an embodiment, multiple signals from each node of two or more nodes are combined into a frame containing reference signals, and the detector is also configured to estimate the course position of frame starts from each node.

In an embodiment, the detector is also configured to estimate the course position by correlating the reference signals with known replicas of these reference signals.

In an embodiment, the detector is also configured to separate reference signals from any other signals in the frame, and the detector is also configured to separate reference signals for each node of the two or more nodes.

In an embodiment, the processor is at least one multipath mitigation processor, wherein the multipath mitigation processor is configured to receive the course locations and separate reference signals and to estimate the relative arrival times of the distance measurement signals from each node.

In an embodiment, the processor is at least one multipath mitigation processor.

In an embodiment, the plurality of signals from each node of the two or more nodes are in a frame, and the detector is also configured to estimate the course position of the frame starts from each node, Wherein the detector is further configured to isolate reference signals for each node of the two or more nodes, and wherein the detector is configured to forward the course locations for each node and separate reference signals to the multipath mitigation processor Wherein the multipath mitigation processor is configured to receive the course locations and separate reference signals and to estimate the relative arrival times of the distance measurement signals from each node.

In an embodiment, a system for tracking and locating one or more wireless devices in communication with a network is configured to receive device signals from one or more wireless network devices, the device signals comprising: The device comprising: a device detector configured to detect device reference signals based on device identification and to detach device reference signals from device signals based on the device identification, wherein the device receiver is configured with a device code determined by device identification of each wireless network device of the one or more wireless network devices, And a plurality of wireless network devices, each of the plurality of wireless network devices comprising: Lt; RTI ID = 0.0 &gt; signals. &Lt; / RTI &gt;

Moreover, the hybrid method may be transparent to the LTE UE location architecture. For example, the hybrid method may operate in the 3GPP TS 36.305 framework.

In an embodiment, the RSTD can be measured and, according to 3GPP TS 36.305, can be passed from the UE to the E-SMLC.

The UL-TDOA (U-TDOA) is currently under study and is expected to be standardized in upcoming Release 11.

Embodiments of UL-TDOA (uplink) are described herein above and also shown in Figs. 16 and 17. Fig. 18 and 19, which are described herein below, provide examples of alternative embodiments of UL-TDOA.

Figure 18 shows an environment that may include one or more DAS and / or femto / small cell antennas. In this exemplary embodiment, each NSAU has a single antenna. As depicted, at least three NSAUs are required. However, additional NSAUs may be added to improve the audibility because each UE must be "listened" by at least three NSAUs.

Furthermore, the NSAU (s) may be configured as receivers. For example, each NSAU receives information over the air, but does not transmit it. In operation, each NSAU may listen to wireless uplink network signals from the UEs. Each of the UEs may be a cell phone, a tag, and / or another UE device.

In addition, the NSAUs can be configured to communicate with the Locating Server Unit (LSU) via an interface, such as a wired service or a LAN. As a result, the LSU can communicate with the wireless or LTE network. The communication may be via a network API, where the LSU may communicate with the E-SMLC of the LTE network, for example, and may use wired services such as LAN and / or WAN.

Optionally, the LSU may also communicate directly with the DAS base station (s) and / or the femto / small cells. Such communications may use the same or modified network APIs.

In this embodiment, a Sounding Reference Signal (SRS) may be used for location purposes. However, other signals may also be used.

The NASUs may convert the UE uplink transmission signals into a digital format, e.g., I / Q samples, and periodically transmit a plurality of transformed signals with a time stamp to the LSU.

The DAS base station (s) and / or femto / small cells may forward one or both of the following data to the LSU:

1) SRS, I / Q samples, and time stamp;

2) a list of served UE IDs; And

3) per-UE SRS schedule with UE ID, scheduling includes SchedulingRequestConfig information and SRS-UL-Config information.

The information communicated to the LSU may not be limited by the above-mentioned information. It may contain any information required to correlate each UE device uplink signal with each UE ID, such as UE SRS.

The LSU function may include obtaining distance measurement calculations and a position fix of the UE. These decisions / calculations may be based on information conveyed from the NSAUs, DAS base stations, and / or femto / small cells to the LSU.

The LSU may also determine timing offsets from the available downlink transmission information conveyed from the NSAUs to the LSU.

As a result, the LSU can provide UE location fixes and other computations and data to the wireless or LTE network. This information can be passed through the network API.

For purposes of synchronization, each NSAU may receive, process, and time stamp samples of the downlink signals. Each NSAU may also periodically transmit a number of such samples to the LSU, including time stamp (s).

Additionally, each NSAU may comprise an input configured for synchronization with the external signal (s).

Figure 19 depicts another embodiment of UL-TDOA. In addition to the components depicted under Figure 18, the environment of this embodiment may include one or more cell towers that may be used in place of DAS base stations and / or femto / small cells. Data from one or more cell towers may be used to obtain the location fix of the UE.

As such, the advantage of this embodiment includes acquiring a location fix with only a single cell tower (eNB). This embodiment may also be configured to operate in a manner similar to that described under Fig. 18, except that one or more eNBs may replace DAS base stations and / or femto / small cells.

One way to find the uplink position of the UE is the cell identification method (CID). In the basic CID method, the UE location can be determined on the cell level. This method is entirely network-based. As a result, the UE, e.g., the handset, does not know that it is being traced. This is a relatively simple method, but it lacks accuracy because the positioning uncertainty is equal to the cell diameter. For example, as illustrated in FIG. 20, any of the handsets 2000 within the cell diameter 2002 of the serving cell tower 2004 may be located at substantially the same location, even if they are not at the same location . The accuracy of the CID method can be improved when combined with a serving sector identification (sector ID) child. 21, a sector ID 2100 is associated with a plurality of handsets 2104, which are known to have a different location from the other handles 2000 in the other sectors of the cell diameter 2002. For example, And identifies the section 2102 within the cell diameter 2002 that it contains.

Further enhancements to the CID method may be possible through the Enhanced Cell ID (E-CID) method, which provides further improvements to the basic CID method described above. One enhancement uses timing measurements to calculate how far the UE is from the eNB (network node). This distance can be calculated as half the round trip time (RTT), or the timing advance (TA) in LTE (LTE TA), the speed of the multiplying light. If the UE is connected, RTT or TA may be used for distance estimation. In both cases: the serving cell tower or section and the UE (on the serving eNB instruction) will measure the timing difference between Rx sub-frames and Tx sub-frames. The UE will report its measurements to the eNB (also under eNB control). LTE Release-9 adds TA Type 2 measurements that depend on the estimated timing advance from receiving the PRACH preamble during the random access procedure. The PRACH (Physical / Packet Random Access Channel) preamble specifies the maximum number of preambles to be transmitted during one PRACH ramping cycle when no response is received from the tracked UE. LTE type 1 TA measurements correspond to RTT measurements, as follows:

RTT = TA (type 1) = eNB (Rx - Tx) + UE (Rx - Tx)

With knowledge of the eNB's coordinates and the height of the serving cell tower antenna, the location of the UE can be calculated by the network.

The E-CID location method is still limited, however, because, in one dimension, the latitude accuracy depends on the distance from the serving cell tower and the sector width and, in another dimension, the error depends on the TA (RTT) measurement accuracy do. Sector width varies with network topology and is affected by propagation phenomena, specifically multipath. Sector accuracy estimates vary from 200 meters to more than 500 meters. The LTE TA measurement resolution is 4 Ts, which corresponds to a maximum error of 39 meters. The actual error in LTE TA measurements is, however, much larger due to calibration inaccuracies and propagation phenomena (multipath), and can reach as much as 200 meters.

As illustrated in FIG. 22, the E-CID method may be further improved in accordance with the addition of a feature known as the arrival angle (AoA). The eNB estimates the direction in which the UE transmits using a linear array of equally spaced antenna elements 2200. Typically, reference signals are used for AoA determination. When the reference signals are received from the UE in two adjacent antenna elements 2200, the reference signals can be phase rotated by an amount dependent on AoA, carrier frequency, and element spacing, as shown in Fig. AoA will require each eNB to have antenna arrays / adaptive antennas. It is also exposed to multipath and topology variants. Nevertheless, sophisticated antenna arrays can significantly reduce the width 2202 of sector 2100, which can lead to better positioning accuracy. In addition, if more than two serving cell towers 2300 (base stations of an eNB with directional antenna arrays) can be used to make a handset AoA determination, as illustrated in FIG. 23, the accuracy can be significantly improved. In this case, the accuracy still experiences multipath / propagation phenomena.

Placing across the antenna arrays / adaptive antennas network across multiple LTE bands requires enormous effort for capital, time, maintenance, and the like. As a result, the antenna arrays / adaptive antennas should not be placed for UE locator purposes. Other approaches, such as signal strength based methods, do not produce significant accuracy improvements. One such signal strength approach is fingerprinting, which requires the creation and continual updating of large capital and reoccurring costs without a false, continuously changing (temporal) fingerprint database, e.g., without significant accuracy improvement. In addition, fingerprinting is a UE-based technology whereby the UE location can not be determined without UE assistance on the UE application level.

A solution to the limitations of other uplink location methods involves the use of AoA capabilities without the need for antenna arrays / adaptive antennas. This embodiment may use TDOA (arrival time difference) location techniques for AoA determination, which may be based on estimating the difference in arrival times of the signals from the sources at the multiple receivers. The particular value of the time difference estimate defines a hyperbola between the two receivers communicating with the UE. When the distance between the receive antennas is small relative to the distance of the emitter (handset) being located, the TDOA is equal to the angle between the baseline of the sensors (receivers antennas) and the incident RF energy from the emitter. If the angle between the base line and the true north is known, the azimuth line (LOB) and / or AoA can be determined.

While conventional location methods using TDOA or LOB (also known as AOA) are known, TDOA location methods are often used to determine LOBs because TDOA reference points are too close to each other to make the accuracy of such techniques unacceptable. Not used. Rather, the LOB is usually determined using directional antennas and / or beam-forming antennas. However, the super resolution methods described herein make it possible to use TDOA for LOB decision while dramatically improving accuracy. It would also be possible, without the reference signal processing techniques described herein, to detect, by way of example, non-serving sectors and / or antennas, reference signals from the UE outside the serving sectors as "listening & . Without the resolution and processing capabilities described herein, it may not be possible to use TDOA for LOB determination since at least two reference points are required (e.g., two or more sectors and / or antennas). Similarly, the UE will not be able to detect on-reference signals from outside the serving sectors, e.g., from non-serving sectors and / or antennas to the UE.

For example, in FIG. 24, two antenna spacing scenarios are illustrated: wide spacing and near (small) spacing. In both scenarios, the hyperbola 2400 and the incident line 2402 intersect at the handset 2000 location, but when the antenna 2404 spacing is wide, this occurs at a more steep angle, . At the same time, when antennas 2404 are close to each other, hyperbola 2400 becomes interchangeable with RF energy incidence or line 2402 of LOB / AoA.

The formula given below can be used to determine the incident RF energy from the emitter, where the time difference in arrival time of the RF energy between the two antennas (sensors) is provided by:

From here:

Δt is the time difference to the seconds;

x is the distance between two sensors at the meter;

는 도들로, 센서들의 기선 및 입사 RF 파 사이에서의 각도이며;

Are the angles between the baseline of the sensors and the incident RF wave;

c is the speed of light.

Multiple positioning strategies are available through the use of a TDOA positioning embodiment, including: (1) when TDOA measurements (multivariate measurements) between two or more serving cells are available, e.g., wide spacing; (2) small antenna intervals, such as, for example, LOB / AoA, when TDOA measurements come only from more than one sector in one or more serving cells; (3) a combination of strategy (2) and strategy (3); And (4) a combination of TA measurements and Strategy (1) to Strategy (3), such as an improved E-CID.

As will be discussed further below, in the case of closely spaced antennas, the TDOA positioning embodiment may use an azimuth line when signals from two or more antennas come from the same cell tower. These signals can be detected in the received composite signal. By knowing the tower position and the azimuth angle of each sector and / or antenna, azimuth lines and / or AoA can be calculated and used in the location process. LOB / AoA accuracy can be affected by multipath, noise (SNR), and so on. However, this effect can be mitigated by the advanced signal processing and multipath mitigation processing techniques described above, which can be based on a super resolution technology. This forward signal processing includes, but is not limited to, signal correlation / correlation, filtering, averaging, synchronous averaging, and other methods / techniques.

The serving cell tower 2500 is comprised of a plurality of sectors, as illustrated in Fig. 25, which typically shows a configuration of three sectors (sector A, sector B, and sector C). The illustrated three sector placement may include one or more antennas 2502 per sector. A single sector, such as sector A, may be in control of the UE (handset) because handset transmissions will be in the main lobe of sector A (the center of the main lobe corresponds to the sector azimuth). At the same time, the handset transmissions will be outside the range of sectors B and C main lobes, e.g., antenna side lobes. Thus, the handset signals will still be present in the output signal spectra of sectors B and C, but will be significantly attenuated for signals from the other handset (s) located in sector B or the main lobes of sector C . Nevertheless, through the use of forward signal processing, as described above and below, it is possible to detect them from the side lobes of neighboring sectors, such as sector B and sector C side lobes, It is possible to obtain a sufficient processing gain. For network-based location purposes, LTE uplink SRS (sounding reference signals) may be used as distance measurement signals.

In other words, the UE uplink reference signal may be in a side lobe of neighboring sector (s) antennas, but the processing gain through the reference signal processing methods described herein may be such that the computation of TDOA between two (or more) Lt; / RTI &gt; The accuracy of this embodiment can be significantly enhanced by the multipath mitigation processing algorithms described above. Thus, the LOB / AOA crossing the loop calculated by the LTE TA timing can provide the UE location within an error ellipse of approximately 20 meters by 100 meters.

The additional positioning error reduction can be achieved when the UE can be listened to by two or more LTE towers, with the processing gains and multipath mitigation techniques described above being very likely. In this case, the intersection of the TDOA hyperbola and one or more LOB / AoA lines may cause a 30 x 20 meter error ellipse (for two sector cell towers). If each cell tower supports three or more sectors, the error ellipse can be further reduced down to 10 to 15 meters. If the UE is listened to by three or more eNBs (cell towers), an accuracy of 5 to 10 meters can be achieved. In high value areas such as malls, commercial complexes, etc., additional subtle cells or passive listening devices may be used to generate the required coverage.

As noted, each of the sectors of the cell tower 2500 may include one or more antennas 2502. In a typical installation, for a given sector, the signals from each antenna are combined at the receiver input of the sector. As a result, for location purposes, two or more sector antennas may be viewed as a single antenna with a combined direction pattern, azimuth angle, and elevation angle. The virtual antenna composite direction and its (main lobe) azimuth and elevation angles can also be assigned to the sector itself.

In an embodiment, received signals (in digital format) from all sectors of each serving cell tower and neighboring serving cell towers are transmitted to a location server unit (LSU) for location determination. In addition, each served SRS schedules and TA measurements per UE is provided to the LSU by each serving cell sector from each serving cell tower. Assuming that each serving cell tower and each neighboring cell tower location coordinates, the number of sectors per tower with each imaginary (synthetic) sector antenna azimuth and elevation angle, and each sector location in the cell tower are known , The LSU may determine the respective UE location for the serving cell towers and / or neighboring cell towers. All of the above-mentioned information may be transmitted over wired networks, e.g., LAN, WAN, etc., using one or more standardized or proprietary interfaces. The LSU may also interface the wireless network infrastructure using standardized interfaces and / or defined interfaces / APIs of the network carriers. The location determination may also be separate between the network node and the LSU, or may be performed only at the network node.

In an embodiment, location determination may be performed at the UE or may be separate between the UE and the LSU or network node. In these cases, the UE may communicate in the air using standard networking protocols / interfaces. Alternatively, the location determination may be performed through a combination of UE, LSU and / or network nodes, or the LSU function may then be used in place of the SUPL server, E-SMLC server, and / or LCS (LoCation Services) (Built-in).

Embodiments of the downlink (DL) location method are the inverse of the uplink (UL) location embodiments described above. In a DL embodiment, a sector may be a transmitter having a transmission pattern, an azimuth angle, and an elevation angle that match the received directional, azimuth, and elevation angles of the sector. Unlike uplink embodiments, in DL embodiments, a UE typically has a single receive antenna. Thus, for the UE, there is no sensor baseline that can be used to determine the incident RF waves. However, the UE can determine the TDOA (s) between the different sectors and consequently the hyperbola (s) (multivariate survey) between the sectors, and since the same cell tower sectors are close to each other, And is interchangeable with LOB / AoA or a line of incident RF energy, as described above with reference. Although LOB / AoA accuracy may be affected by multipath, noise (SNR), etc., this effect can be mitigated through the use of forward signal processing and multipath mitigation processing, which is based on the super resolution technology described above .

As is well known, UE DL location can be realized in similar manners as UE uplink location, except that the RF wave incidence can not be determined from the above formula. Instead, multivariate techniques can be used to determine LOB / AoA for each serving cell tower.

UE DL location embodiments also use reference signals. In the DL case, one approach for this network-based location search may be to use LTE cell-specific reference signals (CRS) as distance measurement signals. Also, position reference signals (PRS) introduced in LTE Release 9 may be used. Thus, the locating can be done using CRS only, PRS only, or both CRS and PRS.

Similar to UE uplink location embodiments, for UE downlink location embodiments, a snapshot of the UE received signal in digital format may be sent to the LSU for processing. The UE may also acquire TA measurements and provide them to the LSU. Alternatively, the TA measurements for each serving UE may be provided to the LSU by respective serving sectors from each serving cell tower (network node). As previously mentioned, it is assumed that each serving cell tower and each neighboring cell tower location coordinates, the number of sectors per tower with each sector transmission pattern azimuth and elevation angle, and the respective sector location in the tower are known , The LSU may determine the respective UE location for the serving cell towers and / or neighboring cell towers. In embodiments, the location determination may be performed at the UE or may be separate between the UE and the LSU or network node. In embodiments, all position determinations may be performed at the LSU or a network node, or may be separate between the two.

The UE will transmit / receive measurement results and other information in the air using standard wireless protocols / interfaces. The exchange of information between the LSU and the network node (s) may be via wired networks, e.g., LAN, WAN, etc., using proprietary and / or one or more standardized interfaces. LSUs can interface wireless network infrastructures using standardized interfaces and / or network carrier defined interfaces / APIs. The location determination may also be separate between the network node and the LSU, or may be performed only at the network node.

For the UE DL location embodiments described above, antenna port mapping information may also be used to determine the location. The 3GPP TS 36.211 LTE standard defines antenna ports for the DL. Separate reference signals (pilot signals) are defined in the LTE standard for each antenna port. Thus, the DL signals also carry the antenna port information. This information is included in the PDSCH (Physical Downlink Shared Channel). The PDSCH includes the following antenna ports: 0; 0 and 1; 0, 1, 2, and 3); Or 5 is used. These logical antenna ports are assigned (mapped) to physical transmission antennas, as illustrated in Fig. As a result, this antenna port information can be used for antenna identification (antenna ID).

For example, antenna port mapping information can be used to determine hyperbola (s) (multivariate) and incident RF waves between antennas (assuming antenna positions are known). Depending on where the location determination is performed, antenna mapping information should be made available to the LSU or UE, or network node. It should be noted that antenna ports are indicated by placing CRC signals in different time slots and different resource elements. Only one CRS signal is transmitted per DL antenna port.

In the case of a MIMO (Multiple Input Multiple Output) arrangement at an eNB or a network node, the receiver (s) will be able to determine arrival time differences from a given UE. (S) (multivariate) for a given eNB antennas and RF waves (LOBs) received at antennas, with knowledge of antenna-to-receiver (s) AoA) &lt; / RTI &gt; Likewise, at the UE, the UE receiver (s) will be able to determine the arrival (s) time differences from the two or more eNBs or network nodes, and the MIMO antennas. it will be possible to determine the hyperbola (s) (multivariate survey) for given eNB antennas and the RF waves (LOB / AoA) incident from the antennas, with knowledge of eNB antenna positions and antennas mappings. Depending on where the positioning is performed; The antenna mapping information should be available to the LSU or UE, or the network node.

There are other configurations that are subsets of MIMO, such as single input multiple outputs (SIMO), single output multiple inputs (SOMI), single input single output (SISO) All of these configurations may be defined / determined by antenna port mapping and / or MIMO antenna mapping information for location purposes.

In an aspect, the embodiments relate to methods and systems for RF-based identification, tracking, and locating of objects, including RTLS. According to one embodiment, the methods and systems utilize geographically distributed clusters of receivers and / or transmitters that are accurately synchronized in time within each cluster, e.g., within 10 nm, while cluster- Time synchronization may be much less accurate or not required at all. Although accurate time synchronization of 10 ns or more is described with respect to one particular embodiment, it is important to note that the predetermined synchronization time required to achieve the correct position depends on the equipment being used. For example, for some wireless system equipment, if an accuracy of 3 m is required for accurate positioning, the predetermined time may require more than 19 ns, but with other radio system equipment and a position accuracy of 50 m It is very sufficient. Therefore, the predetermined time is based on the desired accuracy position for the wireless system. The disclosed methods and systems are a significant improvement over existing implementations of tracking and location DL-OTDOA and U-TDOA techniques that rely on geographically distributed stand-alone (individual) transmitters and / or receivers.

For example, in the DL-OTDOA technique, the relative timing difference between signals from neighboring base stations (eNB) is calculated and the UE position is used in a network with a UE (handset) (A control plane with a dedicated SUPL-based or user plane), or without a network assistance. In DL-OTDOA, when signals from three or more base stations are received, the UE measures the relative timing difference between the signals from the pair of base stations and generates hyperbolic position lines (LOPs). At least three reference points (base stations that do not belong to a straight line) are required to define two hyperbolas. The position of the UE (position fix) is at the intersection of these two hyperboloids (see FIG. 11). The UE location fix is relative to the locations of the RF emitters (antennas) of the base station. For example, when using the LPP (LTE Positioning Protocol, Rel-9), DL-OTDOA locator is UE-assisted and E-SMLC (Evolved Serving Mobile Location Center) is server based.

U-TDOA technology is similar to DL-OTDOA, but roles are reversed. Here, the neighboring location management unit (LMU) calculates the relative arrival time of the uplink signal from the UE (handset), and the UE position can be estimated in the network without the help of the UE. Thus, U-TDOA is LMU-assisted and E-SMLC (Evolved Serving Mobile Location Center) is server-based. Once relative arrival time values from three or more LMUs are available, the E-SMLC server of the network generates hyperbolic location lines (LOPs) and the location (location fix) of the UE (see FIG. 27). The UE location fix is relative to the LMU antenna positions. In an aspect, unlike DL-OTDOA, in the case of U-TDOA the time synchronization of the eNB is not needed - only the LMU (s) will require precise time synchronization for location purposes. By way of example, an LMU is essentially a receiver with computing capabilities. As a further example, the LMU receiver utilizes SDR (Software Defined Radio) technology. In a further example, the LMU may be a small cell, a macro cell, or a special purpose small cell type device that only receives.

Regardless of implementation, correlating the location of the SRS for a particular UE, as provided by the network, will enable UE identification and location. Localization of the SRS can be done at the network level or within the local sector, such as building, small cell or DAS for a combination of small cells and macrocells serving a specific area. If the location of the SRS for the UE is not known a priori, the solution will be able to correlate the location of the UE over the covered area. Doing so will show the location history of the UE moving. In some situations, it may be desirable to determine the location of the UE, even though the network does not provide an indication of where the SRS is located for a particular UE. The location of the UE can be correlated with the SRS by determining the location or proximity of the UE to a known point, thereby correlating the SRS with the UE it transmits. Such a location may be realized through other location / proximity solutions, such as Wi-Fi and Bluetooth. The user may also identify their location either by way of the UE application or by walking to a predetermined location to identify their UE for location solutions.

11 and 27, only macro base stations are shown. Figure 27 also depicts LMUs located in the same location as the base stations. While these representations are valid options, LTE standards do not specify where LMUs can be located, as long as the LMU placement meets the multivariate / trilateration requirements.

In one aspect, common configurations for indoor environments are DAS (Distributed Antenna Systems) and / or small cells, which are low-cost base stations that are highly integrated with RF. The LMU (s) may also be located indoors and / or in a campus-like environment, for example U-TDOA may be used in a DAS and / or small cell environment. In yet another aspect, accurate U-TDOA-based indoor positioning is located outside, without the need to locate LMUs and / or DAS and / or small cells located indoors, for example; Or a combination of macrocells with a reduced number of small cells. Thus, LMUs can be deployed with or without existing DAS and / or small cells. In a further aspect, LMUs can be located in environments where cellular signal amplifiers / booster are used, with or without DAS and / or small cells present.

LTE Release 11 also considers integration of LMUs and eNBs into a single unit. This will, however, reduce the additional burden on time synchronization requirements among small cells if the individual small cell eNBs are geographically dispersed, and wireless / cellular service providers may not be able to communicate with other GPS / It is not ready to meet at.

DAS systems are time synchronized to a much higher degree (precision) than intrinsically geographically distributed macro / mini / small cell / LMUs. Using the DL-DTOA solution in a DAS environment will mitigate the time synchronization issue, but in a DAS environment, a single base station provides multiple distributed antennas, so that multiple antennas may use the same down Link signal. As a result, the conventional DL-OTDOA approach fails because there are no signals for generating identifiable neighboring cells (antennas) with different IDs. Nonetheless, as described in U.S. Patent No. 7,872,583, which is incorporated herein by reference in its entirety, it is to be appreciated that a multi-path mitigation processor and multi-path mitigation techniques / algorithms are used, and using reduced attenuation RF techniques (S), as described in U.S. Serial No. 13 / 566,993, filed August 3, 2012, entitled Multi-Path Mitigation When Distanceing and Tracking Objects, It is possible to use the DL-OTDOA technique when extending the service. However, these coherent algorithms have limitations on the number of antennas that emit signal (s) with the same ID. One solution is to reduce the number of antennas that emit the same ID, e. G., Into multiple DAS antennas into two or more time synchronized clusters with different IDs. This arrangement will increase the system cost (increasing the number of base stations) and will require the handset / UE to support the above mentioned techniques.

Using U-TDOA in a DAS environment will also add cost for adding / installing LMU units. However, no changes to the UE (handset) will be required; Only base station software will need to be upgraded to support U-TDOA functionality. It is also possible to integrate multiple LMUs with the DAS system. Therefore, using the U-TDOA method with LMUs has many advantages when used indoors, in campus environments, and in other GPS / GNSS challenging, geographically restricted environments.

Precise time synchronization among a plurality of geographically dispersed base stations and / or small cells and / or LMUs in indoor and other GPS / GNSS denied environments can be used for outdoor macrocells, e.g. macrocells used in GPS / GNSS friendly environments and / Or time synchronization of LMU equipment. This is because the macrocells in the outdoor environment are increased and have well-known antennas. As a result, the GPS / GNSS signal (s) quality is very good and macrocell antenna transmissions and / or LMU receivers can be synchronized using a GPS / GNSS with a very high accuracy-standard deviation of 10 ns, .

In one aspect, for indoor and other GPS / GNSS reject environments, time synchronization among multiple distributed base stations and / or small cells / LMUs is shared by many base stations and / or small cells and / or LMUs And using an external synchronization source to generate a synchronization signal. This synchronization signal may be derived from GPS / GNSS, e.g. 1 PPS signal, and / or Internet / Ethernet networking, e.g. PTP or NTP. The latter is a low cost solution, but it can not provide the time synchronization accuracy required for the correct location, and the GPS / GNSS derived external synchronization signal (s) is more precise - but with standard deviations below 20 ns, Requirements, such as binding these signals, and are more complex / expensive. In addition, changes to the base station and / or small cell hardware / low level firmware may be required to accommodate external synchronization signals with a higher level of accuracy. In addition, the 20 ns standard deviation is not accurate enough to satisfy the standard deviation of 3 m requirements, for example about 10 ns.

To overcome the above-mentioned limitations, one embodiment includes a plurality of receive antennas 2802 and an LMU device (not shown) having signal channels 2804, as illustrated by the multi-channel LMU high- 2800). One or more signaling channels 2804 may include signal processing components such as RFE (RF front end) 2806, RF down-converter 2808, and / or uplink-position processor 2810 . Other components and configurations may be used. In an aspect, signal channels 2804 are located in the same place in the LMU device 2800 and are tightly time synchronized (e.g., a standard deviation of about 3 ns to about 10 ns). In another example, antennas 2802 from each LMU signaling channel 2804 are geographically distributed (e.g., similar to DAS). As a further example, external time synchronization components (e.g., GPS / GNSS, Internet / Ethernet, etc.) may communicate with the LMU device 2800. Precise time synchronization is achieved more easily in a device (e.g., LMU device 2800) than by trying to strictly synchronize multiple geographically dispersed devices.

By way of example, when two or more multi-channel LMUs (e.g., LMU device 2800) are deployed, time synchronization between these LMUs may be achieved by a low cost and low complexity approach to synchronizing multiple distributed multi- (Using an external source signal). For example, Internet / Ethernet networking synchronization may be used, or a common sensor (device) may be deployed to provide timing synchronization between different multi-channel LMUs.

On the other hand, the multichannel LMU approach reduces the number of hyperbolic position lines (LOPs) that can be used when determining the position fix, but time synchronization improvement overcomes this drawback (see discussion and examples below).

When using multivariate / trilateration methods, the UE positioning accuracy is a function of two factors: the geometrical dilution of precision (GDOP) due to the geometric arrangement of macrocell towers / small cells / LMUs, and Single-distance measurements σ _R-pseudo accuracy of measurements (see 2003, Gunter Seeber, Satellite Geodesy):

와 같으며(1973, H. B. LEE의, 쌍곡선 다변측량 시스템들의 정확도 제한들); 여기에서 셀룰러 네트워크들의 경우에, N은 UE(DL-OTDOA의 경우에)에 의해 "들을 수 있는" 방출기들(매크로 셀 타워들/스몰 셀들/DAS 안테나들)의 수 또는 UE 업링크 송신(U-TDOA의 경우에)을 "들을" 수 있는 LMU들/LMU 수신 채널들의 수이다. 그러므로, UE 위치 에러의 표준 편차는 다음과 같이 산출될 수 있다:GDOP is a function of the geographic distribution of the transmit antennas (in the case of DL-OTDOA) or the receive antennas (in the case of U-TDOA). In the case of a regularly positioned antenna, the two-dimensional GDOP estimation

(1973, HB LEE, Limits of Accuracy of Hyperbolic Multipath Systems); Where N is the number of "hearable" emitters (macrocell towers / small cells / DAS antennas) by the UE (in the case of DL-OTDOA) or UE uplink transmission (U LMU &lt; / RTI &gt; receive channels that can "hear &quot; Therefore, the standard deviation of the UE position error can be calculated as: &lt; RTI ID = 0.0 &gt;

Assume that eight geographically dispersed (indoor) single receive channel LMUs (regularly located) detect UE uplink transmissions and these LMUs are synchronized via a 1 PPS signal (e.g., a standard deviation of 20 ns) . In this case there will be seven independent LOPs where N = 8 and can be used for UE location fixes. Assuming further that the distance measurement error standard deviation ( _R ) is 3 meters (about 10 ns); The accuracy of the single distance measurement measurements is then:

Where σ _SYNC is the external time synchronization signal standard deviation (20 ns).

In this case (N = 8), the standard deviation of single distance measurement measurements and UE position error (? _POS ) is equal to 4.74 meters.

로서 획득된다. For example, if two or four receive channel LMUs (e.g., a multi-channel LMU device 2800) with regularly located distributed antennas detect UE uplink transmissions, each LMU may be subjected to three strictly time synchronization Will generate a set of LOPs (e.g., a standard deviation of about 3 ns); N = 4 for three independent LOPs. In this case, two UE position fixes are generated, each with a standard deviation error (? _POS ) of 3.12 meters. Combining these two position fixes by averaging and / or by other means / methods will further reduce the UE position fix error. The estimate of work is that the error reduction is proportional to the square root of the number of UE position fixes. In the present disclosure, this number is equal to 2 and the final UE position fix error (sigma _{POS_FINAL} ) is 2.21 meters,

.

In an aspect, several multi-channel LMUs (e.g., LMU device 2800) with mitigated synchronization between these multi-channel LMUs may be used for indoor and other GPS / GNSS reject environments. By way of example, within a multi-channel LMU device, LMUs can be tightly synchronized (e.g., a standard deviation between about 3 ns and about 10 ns). Another embodiment is a rack-mount enclosure (Figures 31, 32, and 33) in which small cells with multiple single channel small cell / LMU and / or integrated LMU device electronics (LMU functionality embedded in the eNB) And / or can be clustered (e.g., integrated, located in the same place, etc.) in a cabinet, e.g., a 19 inch rack. Each single channel device antenna can be geographically dispersed, as in DAS. Devices in a cluster can be tightly time synchronized (e.g., a standard deviation of 10 ns or less). Multiple rack mount enclosures can be synchronized per communication requirement, e.g., VoLTE, thereby allowing a low cost and low complexity approach to be used. Precise (strict) time synchronization among multiple devices clustered (integrated) within a rackmount enclosure / cabinet is easier to achieve and less expensive than when strictly time synchronizing multiple geographically dispersed devices.

In yet another aspect, multiple LMUs may be integrated with the DAS system as illustrated in FIG. By way of example, LMU receivers may share received signal (s) generated by respective DAS antennas, which share, for example, DAS antennas. The actual distribution of these received signals depends on the DAS implementation: active DAS versus passive DAS. However, the LMU and DAS integrated embodiments have the advantage of having the LMU receiver channel and sharing the received signal (s) generated by each DAS antenna and matching (correlating) each DAS antenna coordinate with the corresponding LMU / Lt; RTI ID = 0.0 &gt; almanac. &Lt; / RTI &gt; Again, using a clustering approach and / or multi-channel LMU (s) is a preferred approach for LMU and DAS integration.

Also, in a similar manner, it is possible to share the received signal (s) generated by each small cell antenna with the LMU receiver channel. Here, the time synchronization of the small cell can be mitigated and, for example, LMU / LMU channels will require precise time synchronization, although it does not require to meet the locating requirement. Using a clustering approach and / or multi-channel LMU (s) is the preferred approach for LMU (s) for this option.

The integration of LMUs and eNBs into a single unit is a cost advantage over the combination of standalone eNB and LMU devices. However, unlike the integrated LMU and eNB receivers, the standalone LMU receive channel does not need to process the data payload from the UE. Moreover, since the UE uplink distance measurement signals (SRS, sounding reference signal in the case of LTE) are repeatable and time synchronized (for the serving cell), each independent LMU receive channel can support two or more antennas (Which may be time multiplexed with it), for example, two or more small cells. This can result in a reduction in the number of LMUs (in the case of small cells / DAS and / or other U-TDOA locating environments) and the cost of the system (see also Fig. 28).

If the wireless / cellular network E-SMLC server does not have the functionality required for DL-OTDOA and / or U-TDOA technologies, then this functionality may be communicated to the UE and / or LMU and wireless / cellular network infrastructure and / (See Figures 29 and 30). Other configurations may be used.

In another aspect, one or more LMU devices (e.g., LMU 2802) may be deployed with a WiFi infrastructure, for example, as illustrated in FIG. Alternatively, the listening device may be used to monitor the LMU antenna in the same manner as the WiFi infrastructure. As such, channel antennas serving LMU devices and / or LMUs may be located in the same location as one or more WiFi / listening devices 3500, such as one or more WiFi access points (APs). By way of example, WiFi devices 3500 may be geographically dispersed.

In one embodiment, the WiFi device 3500 may be connected to a power source. The RF analog portion 3502 (e.g., circuitry) of one or more LMU devices or channels may be integrated with the LMU antennas so that the RF analog portion 3502 may share power with the WiFi device 3500 35). By way of example, the RF analog portion 3502 of the LMU device or channel may be coupled to the uplink-position processor circuit (e.g., uplink-position processor 2810) via a cable, . As a further example, this embodiment enables an improved signal-to-noise ratio (SNR) because there may be signal amplification between the antenna and the RF analog portion 3502 and the interconnect cable on the baseband circuit . In addition, since the RF analog portion 3502 can down-convert the received signal (e.g., down the baseband) and the baseband signal frequencies are of several sizes smaller than the received signal at the antenna, . The relaxation of these cable requirements can lead to a reduction in the cost of connections and can significantly increase the transmission distance.

It is understood that the distance measurement signals are not limited to SRS only and may use other reference signals, including MIMO, CRS (cell-specific reference signal), and the like.

It should be apparent to those of ordinary skill in the art that when the different embodiments of the systems and methods are described accordingly, certain advantages of the described methods and apparatus have been achieved. In particular, it should be understood by those of ordinary skill in the art that systems for tracking objects and locating their positions can be assembled using FPGA or ASIC and standard signal processing software / hardware combinations at very small incremental costs do. Such systems are useful in locating people in a variety of applications, such as indoor or outdoor environments, harsh and hostile environments.

It should also be understood that various modifications, adaptations, and alternative embodiments thereof may be made within the spirit and scope of the present invention.

Claims (37)

A method for determining the location of one or more user equipment (UE) in a wireless system,
Receiving reference signals via channels located at two or more same locations;
Synchronizing the timings of the channels located at said two or more same locations within a standard deviation below a predetermined time based on a desired accuracy of location for said wireless system; And
And using the received reference signals to calculate the location of at least one UE among the one or more UEs. The method according to claim 1,
Further comprising using a multipath mitigation processor configured to receive and process the received reference signals, wherein the multipath mitigation processor performs a high-resolution spectral estimation analysis to reduce spatial ambiguity associated with the received reference signals Wherein the high-resolution spectral estimation comprises estimating a model size for a plurality of frequency components of the received reference signals and estimating a model size of the at least one UE based on a distribution of a plurality of artificial frequencies of the frequency components. And calculating a position of the at least one user equipment. The method of claim 2,
Wherein the high-resolution spectral estimation analysis uses one or more high-resolution spectral estimation algorithms. The method of claim 3,
Wherein the one or more high-resolution spectral estimation algorithms include a Matrix Pencil algorithm. The method according to claim 1,
Wherein each of the channels located at the two or more same locations comprises a location management unit card or a small cell. The method according to claim 1,
Wherein the predetermined time is between about 3 ns and about 10 ns. The method according to claim 1,
Wherein the received reference signals are uplink reference signals, downlink reference signals, distributed antenna system reference signals, or a combination thereof. The method according to claim 1,
The wireless system comprising one or more nodes, each of the one or more nodes including at least one sector, wherein a sector of each node is configured to communicate with a locate server unit (LSU) Wherein the step is performed by the LSU or a combination of the one or more nodes and the LSU. The method according to claim 1,
Wherein the wireless system comprises one or more nodes, each of the one or more nodes comprises at least one sector, and wherein a sector of each node is configured to communicate with a locating server unit (LSU) Wherein the method is performed by the UE, the LSU, the one or more nodes, or a combination thereof. The method of claim 9,
Wherein the at least one UE is configured to communicate with the LSU. The method of claim 10,
Wherein the at least one UE, the LSU, the at least one node, or a combination thereof is configured to support multipath mitigation and reference signal processing to yield the location of each UE, Way. The method according to claim 1,
Wherein the wireless system is configured to include the functionality of an LSU in a network SUPL server, an E-SMLC server, a LoCation Services (LCS) system, or a combination thereof. The method according to claim 1,
Wherein the wireless system comprises an LSU and one or more nodes, the LSU being configured to interface the one or more nodes and the network infrastructure of the wireless system. The method according to claim 1,
Wherein the utilizing comprises using one or more line of position (LOP). &Lt; Desc / Clms Page number 21 &gt; The method according to claim 1,
Wherein the reference signals are received from a geographically dispersed antenna. A method for determining the location of one or more user equipment (UE) in a wireless system,
Receiving reference signals via a first location management unit having channels located at two or more same locations;
Synchronizing the timings of the channels located at said two or more same locations within a first standard deviation below a first predetermined time based on a desired accuracy of location for said wireless system;
Receiving reference signals via a second location management unit having channels located at two or more same locations;
Synchronizing the timings of the channels located at the same location of the second location management unit within a second standard deviation below a second predetermined time based on the desired accuracy of the location for the wireless system; And
Using the received reference signals from the first location management unit and the received reference signals from the second location management unit to calculate the location of at least one UE among the one or more UEs A method for determining a location of one or more user equipment. 18. The method of claim 16,
Further comprising using a multipath mitigation processor configured to receive and process the reference signals from the first location management unit and the second location management unit, wherein the multipath mitigation processor is configured to: Using a high-resolution spectral estimation analysis to reduce spatial ambiguity associated with the received reference signals of the second location management unit, and wherein the high-resolution spectral estimation uses the first location management unit and the second location management unit Estimating a model size for a plurality of frequency components of the received reference signals and computing the position of the at least one UE based on a distribution of a plurality of artificial frequencies of the frequency components. A method for determining a position of a user equipment. 18. The method of claim 17,
Wherein the high-resolution spectral estimation analysis uses one or more high-resolution spectral estimation algorithms. 19. The method of claim 18,
Wherein the at least one high-resolution spectral estimation algorithm comprises a matrix pencil algorithm. 18. The method of claim 16,
Wherein the first predetermined time and the second predetermined time are between about 3 ns and about 10 ns. 18. The method of claim 16,
Wherein the second predetermined time is between about 3 ns and about 10 ns. 18. The method of claim 16,
Wherein the received reference signals are uplink reference signals, downlink reference signals, distributed antenna system reference signals, or a combination thereof. 18. The method of claim 16,
Wherein the utilizing comprises using one or more location lines (LOPs). 18. The method of claim 16,
Wherein the first location management unit receives reference signals from a geographically dispersed antenna. 18. The method of claim 16,
Wherein the second location management unit receives reference signals from a geographically dispersed antenna. 18. The method of claim 16,
Wherein the first predetermined time and the second predetermined time are greater than about 10 ns. A method for determining the location of one or more user equipment (UE) in a wireless system,
Receiving reference signals via a location management unit having channels located at two or more same locations, wherein the channels located at two or more same locations are strictly time-synchronized with each other; And
And using the received reference signals to calculate a position of at least one UE among the one or more UEs. 28. The method of claim 27,
Wherein each of the channels located at the two or more same locations comprises a location management unit card or a small cell. 28. The method of claim 27,
Each of the channels located at the two or more same locations being integrated with the same rack mount system. 28. The method of claim 27,
Wherein the channels located at the same location are synchronized within a standard deviation of a predetermined time based on a desired accuracy of the location for the wireless system. 32. The method of claim 30,
Wherein the predetermined time is between about 3 ns and about 10 ns. 28. The method of claim 27,
Wherein the utilizing comprises using one or more location lines (LOPs). 28. The method of claim 27,
Wherein the reference signals are received from a geographically dispersed antenna. 28. The method of claim 27,
Wherein the reference signals are received from a shared group of antennas communicating with channels located at the two or more same locations. 28. The method of claim 27,
Wherein the antenna that serves the location management unit or the location management unit is located in the same location as the WiFi device. 36. The method of claim 35,
Wherein the location management unit shares power with the WiFi device. 36. The method of claim 35,
Wherein the antenna serving the location management unit shares power with the WiFi device.

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