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WO2008125125A2 - Acquisition de données à partir de réseaux non uniformes fondée sur des croisements d'onde sinusoïdale - Google Patents

Acquisition de données à partir de réseaux non uniformes fondée sur des croisements d'onde sinusoïdale Download PDF

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Publication number
WO2008125125A2
WO2008125125A2 PCT/EE2008/000008 EE2008000008W WO2008125125A2 WO 2008125125 A2 WO2008125125 A2 WO 2008125125A2 EE 2008000008 W EE2008000008 W EE 2008000008W WO 2008125125 A2 WO2008125125 A2 WO 2008125125A2
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WO
WIPO (PCT)
Prior art keywords
signal
spatial
coefficients
array
interference
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Ceased
Application number
PCT/EE2008/000008
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English (en)
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WO2008125125A3 (fr
Inventor
Ivars Bilinskis
Mart Min
Aleksanders Ribakovs
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Tallinn University of Technology
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Tallinn University of Technology
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Filing date
Publication date
Application filed by Tallinn University of Technology filed Critical Tallinn University of Technology
Priority to EEP200900083A priority Critical patent/EE05618B1/xx
Publication of WO2008125125A2 publication Critical patent/WO2008125125A2/fr
Publication of WO2008125125A3 publication Critical patent/WO2008125125A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M1/00—Analogue/digital conversion; Digital/analogue conversion
    • H03M1/12—Analogue/digital converters
    • H03M1/124—Sampling or signal conditioning arrangements specially adapted for A/D converters
    • H03M1/1245—Details of sampling arrangements or methods
    • H03M1/1265—Non-uniform sampling
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M1/00—Analogue/digital conversion; Digital/analogue conversion
    • H03M1/12—Analogue/digital converters
    • H03M1/1205—Multiplexed conversion systems
    • H03M1/123—Simultaneous, i.e. using one converter per channel but with common control or reference circuits for multiple converters
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M1/00—Analogue/digital conversion; Digital/analogue conversion
    • H03M1/12—Analogue/digital converters
    • H03M1/50—Analogue/digital converters with intermediate conversion to time interval

Definitions

  • the invention relates to methods of data acquisition from large aperture sensor arrays, namely a method based on non-uniform arrangement of sensors and distributed digitizing of sensor signals utilizing crossings with a reference sine wave.
  • sensor arrays can be smart antennas and hydrophones in radar and sonar systems and radio astronomy equipment, also electrodes of several mapping and imaging devices for medical dignosing (e.g. EEG and ECG), including also impedance spectroscopy and tomography.
  • the focus is directed on the complexity reduction of sensor array systems including subsystem for massive data acquisition from a large quantity of sensors.
  • the sensor arrays have regular linear and circular configurations, in which the spacing of sensor is uniform ( P. S. Naidu, "Sensor Array Signal Processing, CRC Press LLC, Boca Raton, 2001).
  • Our task is reducing the number of sensors surviving the degree of resolution of the array.
  • This task can be solved using nonuniform placement of sensors in the array together with applying the specific signal processing method, which is suited for application of the Digital Alias-free Signal Processing (DASP) technology (Wars Bilinskis, “Digital Alias-free Signal Processing", Wiley, NJ, published in May 2007) together with distributed analog-to-digital convertion (ADC), described in EP 1 170459A1 to Bilinskis et al, published on 13.09.2006.
  • DASP Digital Alias-free Signal Processing
  • ADC analog-to-digital convertion
  • the objective of the invention is, therefore a new system for sensor placement and an improved method for digitizing and processing of sensor signals.
  • the proposed approach to data acquisition is discussed in the context of array signal processing. Attention is drawn to the fact that the DASP technology is well suited both for the data acquisition from the sensors in arrays and for processing the obtained data. The point is that application of this technology should lead to significant complexity reduction of large aperture arrays. As it is shown further, nonuniform spacing of sensors in the arrays, under certain conditions, makes it possible to reduce the number of sensors needed and, additionally, the sensor signal data acquisition could be also simplified as suggested.
  • Fig.l depicts diagrams of a regular (a) and a pseudo-randomised (b) sensor arrays.
  • Fig.2 describes a block diagram of a system for data acquisition from an array of nonuniformly spaced sensors
  • Fig. 3 depicts a block diagram of the massive data acquisition system based on sine-wave crossings applicable for array signal sampling.
  • Fig. 4 is a spatial spectrum calculated for signals from a regular array of 128 uniformly spaced sensors.
  • Fig. 5 is a spatial spectrum calculated for signals from a regular array of 32 uniformly spaced sensors.
  • Fig. 6 is a spatial spectrum calculated for signals from an irregular array of 32 non-uniformly spaced sensors.
  • Fig. 7 is a spatial spectrum obtained after the first iteration by processing the spectrum shown in Fig. 6.
  • Fig. 8 is a spatial spectrum obtained after the second iteration by processing the spectrum shown in Fig. 6.
  • Fig. 9 is a spatial spectrum obtained after the fifth iteration by processing the spectrum as in Fig. 6
  • Conditions for data acquisition from sensor arrays depends both on the respective array design and on the algorithms for following processing of these data. Let us consider a typical case of data acquisition from a large aperture linear array of .K sensors assuming that processing of data is based on the temporal spectrum analysis that is carried out first before the spatial spectrum analysis. Then N snapshots of received signal sample values have to be acquired and Discrete Fourier Transforms (DFT) of them have to be performed at the first stage of the array signal processing. The problem is that the number of the sensors in the array might be large and therefore large number of sensor signals has to be sampled simultaneously.
  • DFT Discrete Fourier Transforms
  • the interval d should be sufficiently small and the number of sensors sufficiently large.
  • the complexity of the array signal processing system evidently is directly proportional to the quantity of the sensors in the array.
  • the value of d defines also the row of indistinguishable wavenumbers.
  • Fig 1 (b) A diagram of such a pseudo-randomized sensor array is given in Fig 1 (b). Note that the sensors in it are placed on the same grid as in the regular array of Fig 1 (a) with the distance between the grid lines equal to d. The difference is in the irregularity of the distance between the sensors.
  • the pseudo-randomized array may be considered as the original regular array with about two thirds of the sensors left out so that the mean distance between the sensors in the second array is equal to 4d. Therefore such irregular, or pseudo-randomized array, in this particular case, contains four times less sensors within the same aperture.
  • the following discussion of the pseudo- randomized array application potential assume exploitation of such an array.
  • Fig 2 describes a block diagram of a system for data acquisition from the array of nonuniformly spaced sensors 1.
  • the signal from the array is given to a K-channel uniform sampler and distributed digitizer 2.
  • DFT is performed in Discrete Fourier Transformer 3.
  • spatial Fourier Spectrum is obtained from Matrix data converter 4.
  • the distributed ADC structure for remote sampling has a number of attractive advantages for massive data acquisition.
  • a straightforward application of this structure is not possible here because the signal sample values are estimated on the basis of the signal and the reference sine-wave crossings are obtained at signal-dependent time instants.
  • this type of sampling is essentially nonuniform and that is not acceptable for acquisition of data from the sensor arrays. Snapshots have to be taken periodically and simultaneously at time instants dictated by the specifics of the array functioning.
  • the basic data acquisition system based on sine-wave crossings has to be modified as shown in Fig 3.
  • Sample & Hold devices are included in the input channels for taking the sample values of sensor signal simultaneously and for holding the obtained analog sample values long enough for execution of the following generations of single-shot pulses on the sine-wave crossings.
  • the sensor sample values are encoded as timing of the reference crossing instant. This timing information then is transmitted to the central part of the data acquisition system where the sensor signal digital sample values are reconstructed.
  • the array signal sample value at the £-th sensor at time instant t is given as
  • M is the number of the array signal component with the carrier frequency f x
  • d ⁇ is the coordinate of the sensor
  • the m-th spatial frequency is defined as
  • ⁇ m is the arrival angle of the m-th signal and C L is the propagation speed of the signals.
  • DFT discrete Fourier transform
  • sequence of coefficients a(d k ) is the in-phase component of the spatial signal
  • sequence of coefficients ⁇ d k ) is the quadrature component of the spatial signal
  • the arrival angle of signals is given as
  • Signal Direction-of-Arrival (DOA) estimation belongs to the list of the essential sensor array functions based on the spatial spectrum analysis.
  • An example of such a spatial spectrogram obtained from a linear array of 128 equidistantly spaced sensors is given in Fig 4.
  • Computer simulations were carried out for the case where three sine wave signals are received by the array.
  • the positions of the sensors in the array is randomized as shown in Fig Ib and their number is reduced to 32.
  • the spatial spectrogram obtained in this case using the direct DFT approach , is given in Fig 6.
  • the spatial aliases are suppressed in this case, but the signal to noise ratio is poor as the spectrogram is corrupted by strong fuzzy aliasing peaks.
  • principially different approach to the processing of array signal has to be used.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
  • Analogue/Digital Conversion (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

L'invention concerne un nouveau procédé d'acquisition de données à partir de réseaux de capteurs à grande ouverture mis en oeuvre par randomisation délibérée d'échantillonnage temporel et spatial de signaux de réseaux et au moyen de procédés itératifs. L'invention propose également une structure de convertisseur analogique-numérique répartie qui comprend des dispositifs échantillonneurs-bloqueurs pour l'échantillonnage simultané de capteurs.
PCT/EE2008/000008 2007-04-17 2008-04-16 Acquisition de données à partir de réseaux non uniformes fondée sur des croisements d'onde sinusoïdale Ceased WO2008125125A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EEP200900083A EE05618B1 (et) 2007-04-17 2008-04-16 Meetod siinussignaalide ristumisel p?hinevaks andmeh?iveks ebahtlase asetusega sensorv?redelt

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US91226907P 2007-04-17 2007-04-17
US60/912,269 2007-04-17

Publications (2)

Publication Number Publication Date
WO2008125125A2 true WO2008125125A2 (fr) 2008-10-23
WO2008125125A3 WO2008125125A3 (fr) 2009-03-19

Family

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PCT/EE2008/000008 Ceased WO2008125125A2 (fr) 2007-04-17 2008-04-16 Acquisition de données à partir de réseaux non uniformes fondée sur des croisements d'onde sinusoïdale

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EE (1) EE05618B1 (fr)
WO (1) WO2008125125A2 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108181625A (zh) * 2018-01-30 2018-06-19 电子科技大学 一种基于非均匀频谱估计的钻孔雷达成像方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5373295A (en) * 1992-09-02 1994-12-13 General Electric Company Digital interface circuit for high dynamic range analog sensors
US6477553B1 (en) * 1999-01-13 2002-11-05 Philip Druck Measurement scale for non-uniform data sampling in N dimensions
US6594367B1 (en) * 1999-10-25 2003-07-15 Andrea Electronics Corporation Super directional beamforming design and implementation
US20040125893A1 (en) * 2002-12-17 2004-07-01 Saeed Gazor Methods and systems for tracking of amplitudes, phases and frequencies of a multi-component sinusoidal signal
WO2004066504A1 (fr) * 2003-01-17 2004-08-05 Koninklijke Philips Electronics N.V. Systeme et procede de conversion analogique-numerique et systeme de traitement de signaux dans lequel le systeme de conversion est mis en oeuvre
WO2005122556A1 (fr) * 2004-06-07 2005-12-22 Canon Kabushiki Kaisha Dispositif et systeme de prise de vues
EP1701459A1 (fr) * 2005-03-09 2006-09-13 Institute of Electronics and Computer Science of Latvian University Procédé d'acquisition de données provenant de plusieurs sources de signaux analogiques

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108181625A (zh) * 2018-01-30 2018-06-19 电子科技大学 一种基于非均匀频谱估计的钻孔雷达成像方法

Also Published As

Publication number Publication date
EE200900083A (et) 2010-02-15
WO2008125125A3 (fr) 2009-03-19
EE05618B1 (et) 2012-12-17

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