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WO2008065257A1 - Techniques d'amélioration de la fiabilité d'un système de prédiction - Google Patents

Techniques d'amélioration de la fiabilité d'un système de prédiction Download PDF

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WO2008065257A1
WO2008065257A1 PCT/FI2007/050651 FI2007050651W WO2008065257A1 WO 2008065257 A1 WO2008065257 A1 WO 2008065257A1 FI 2007050651 W FI2007050651 W FI 2007050651W WO 2008065257 A1 WO2008065257 A1 WO 2008065257A1
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context
estimation system
score
similarity
performance
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Kari Vasko
Pauli Misikangas
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Ekahau Oy
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/021—Calibration, monitoring or correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0244—Accuracy or reliability of position solution or of measurements contributing thereto
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services

Definitions

  • the invention relates generally to techniques for improving predictability of unknown values, particularly when such techniques are used in control of a physical system.
  • the invention relates particularly to a context estimation system for an environment including at least one observable context-dependent physical quantity.
  • the context estimation system is capable of outputting a context estimate for a target object using an observation made by a sensing device attached to the target object, wherein the observation comprises values for one or more physical quantities, and wherein the context estimate is used by a context-aware application to trigger context-related actions.
  • a method for implementing such a context estimation system comprises defining two or more contexts, wherein each context defines the circumstances under which observations of the physical quantities are made.
  • the method further comprises providing calibration data for each context, wherein the calibration data for a context comprises several observations, each of which is made under the circumstances defined by the context and comprises values for one or more physical quantities. Then a context model is constructed for each context using the calibration data of the context, wherein the context models are used by a context score function taking an observation and a context as input and returning a score indicating the likelihood that the given observation is made under the given context.
  • the context relates to the location of a target object in the environment.
  • Location estimation is carried out by a position- ing system, which is a non-restricting example of a context estimation system.
  • the positioning system is taught to estimate a target devices' location given a set of values of location-dependent physical quantities observed at the target device's location.
  • Such techniques are described in commonly owned patent applications, some of which are listed at the end of this description.
  • Another illustrative but non-restrictive practical application of the invention relates to automatic plant growing in a greenhouse.
  • model-building step may not provide an adequate estimate of the reliability of the context estimation capabilities of the context estimation system.
  • Calibration data D[i] is used to teach a context model M[i] and the whole prediction systems consists of the context models M[1], M[2]...,M[k]. After that, it is assessed how similar the context models M[1], M[2] M[k] are.
  • KL-divergence and its derivatives are theoretically more solid and they are based on evaluating a distance measure between the probability distributions.
  • an object would be to evaluate distance between probability distributions of different contexts.
  • Both of the specified prior-art approaches suffer from certain limita- tions, however.
  • An aspect of the invention is a method for generating a computer- implemented context estimation system for an environment including at least one observable context-dependent physical quantity, wherein the context esti- mation system comprises a data processor and memory and is capable of out- putting a context estimate for a target object using an observation made by a sensing device attached to the target object, wherein the observation comprises at least one value for one or more physical quantities, and wherein the context estimate i used by a context-aware application to trigger one or more context-related actions, the method comprising: a) defining two or more contexts, wherein each context defines the circumstances under which observations of the physical quantities are made; b) providing calibration data for each context, wherein the calibration data for a context comprises several observations, each of which is made under the circumstances defined by the context and comprises values for one or more physical quantities; c) constructing a context model for each context using the calibration data of the context, wherein the context models are used by a context score function taking an observation and a context as input and
  • Another aspect of the invention is a set of computer-readable media for a data processing system which comprises a model construction section and a context estimation section.
  • the set of computer-readable media comprises first computer program instructions whose execution in the model construction sec- tion causes the model construction section to carry out steps a) through d) of claim 1 ; and second computer program instructions whose execution in the context estimation section causes the context estimation section to carry out step f) according to claim 1.
  • Yet another aspect of the invention is a data processing system which comprises a model construction section and a context estimation section, wherein the data processing system further comprises the above-defined set of computer-readable media.
  • Some implementations of the present invention aim at providing improved analysis of similarity of competing statistical models.
  • the focus is not in model selection but introduction of a new approach to assess ambiguity of the statistical prediction system.
  • the inventive approach is based on a new similarity measure called "T-similarity".
  • T-similarity a new similarity measure
  • Various specific embodiments and implementations of the invention relate to local and global measures of ambiguity of the prediction system that are based on the T-similarity measure.
  • Other embodi- ments and implementations relate to a generalization of the T-similarity measure called (T,f,s)-similarity measure that can be used to assess how noise tolerant the prediction system is.
  • the invention can be implemented as a computer-implemented method for providing a reliability estimate relating to a property set of a target device in an environment, wherein the property set comprises location.
  • the method comprises, prior to determining the property set of the target device, determining a value for each of one or more location- dependent physical quantities in several locations in the environment, wherein the values for the one or more location-dependent physical quantities are determined by calibration measurements and/or simulation.
  • the method further comprises using the determined values for the one or more location-dependent physical quantities to teach a statistical model to model the environment such that the statistical model is operable to estimate the property set of the target device given a set of location-dependent physical quantities observed at the target device's location.
  • the statistical model so taught is used to provide a reliability estimate relating to the property set of the target device in the environment.
  • a positioning system models a positioning environment which comprises two regions called A and B.
  • the positioning system is an exam- pie of a context estimation system and a region is an example of a context.
  • a context may also be defined as a composition of other contexts, for instance a set of regions.
  • the aim or the positioning system is to predict whether the target object is inside the region A or B.
  • a first phase in the setup of the positioning system comprises collecting calibration and/or simulation data from regions A and B.
  • a second phase comprises teaching a positioning model coupled to the positioning system on the basis of the calibration data.
  • a third phase comprises collecting test data in order to get a rough understanding of the predictive performance of the system.
  • a problem in this scenario is that the positioning model coupled to the positioning system must be more or less completed (taught on the basis of the calibration or simulation data) before its performance can be assessed. If it turns out that the positioning model comprises ambiguous regions, the model setup must be started with new parameters. Thus, it would be beneficial to know whether the positioning model which was taught on the basis of the calibration data contains ambiguous regions (regions similar to one another), before the test data is collected. This is because in the real-world cases it is not realistic to assume that extensive test data will always be available from all regions (or more generally from all contexts).
  • the model comprises 1000 regions
  • a huge amount of resources will be needed to collect reliable test data from every region.
  • Information regarding noise tolerance is essential, if it is likely that the signal values will be over-dispersed with respect to the calibration conditions. For instance, in hospitals, it is not possible to collect calibration data from the operation rooms while they are being used for medical operations, and WLAN signal strength data values are over-dispersed during actual use of the WLAN network.
  • the inventive technique can be used in an automatic detection of problematic contexts, eg, regions in which the context estimation system, such as a positioning system, is likely to be unreliable.
  • the inventive technique can use actual test data but is does not necessarily need any.
  • the automatic detection of problematic regions can be performed solely on the basis of calibration or simulation data.
  • the invention gives a technique to assess model-based noise-tolerance of the system via a (T,f,s)-similarity measure. This is valuable in order to make sure that the context estimation system is reliable and has a high noise tolerance level.
  • the inventive technique can automatically detect contexts, such as regions, where an increased risk for large errors exists on the basis of calibration data and the context models.
  • the inventive technique is more generic, as will be seen later in this document.
  • a positioning application will be used as an illustrative but non-restrictive practical application.
  • context A be "zone A”
  • context B is "zone B”.
  • D(X) calibration measurements that were recorded in the context X, where X is either A or B.
  • ContSys be a context estimation system of the invention, wherein the positioning system is a non-restricting example of a context estimation system.
  • ContSys gets as an input a measurement of the explaining variables, eg WLAN signal strengths, and its output is a predicted context.
  • score functions include an expected utility, conditional probability, fuzzy logic approaches and rough set theory. From now on, we will use as an illustrative, but not restricting example, the statistical decision theory. Those skilled in the art know that in the statistical decision theory the optimal decision is to choose a prediction that has maximal utility. We assume, for simplicity's sake, the classic 0/1 -utility function, which yields
  • x) - Pr(A,x)/Pr(x) Pr(A)Pr(x
  • this implementation is based on answering to the following question 1 : "If the calibration data of the context A was given to the context estimation system, how often would ContSys predict the wrong context B?" [0022]
  • This implementation is able to exploit training data as semi test data, since calibration data of the context A is not used in the construction process of the context B's model.
  • the model of the context A is based on its own calibration data and, accordingly, it is expected that ContSys should give a higher score for the context A than for the context B. If this does not hold, contexts A and B are mixed up with one another.
  • the inventive prediction technique can answer the above-presented question 1 by calculating the following probability quantity:
  • Pr[x is classified to context B
  • x is a calibration sample of a A] similarity(A ⁇ B
  • x is a random observation from A's calibration data set D(A). It will be apparent that the higher the probability quantity is in equation [4], the more probable it is in the light of the calibration data that the context A is mixed up with the context B.
  • An exemplary technique to compute a similarity measure defined in equation [4] is simply to loop over all observations of the context A and then calculate a proportion how often ContSys made a wrong prediction context B. [0026] The following quantity is known as the (Bayes) classification error:
  • Pr[ContSys makes prediction error] Pr[x is classified to context B
  • a target object is a plant and an environment is a greenhouse.
  • an environment is a greenhouse.
  • Illustrative but non- restrictive examples of the corresponding context-dependent physical quantities are local ground moisture and a colour of a plant. For each plant, moisture and colour is measured independently in order to estimate a context of a plant by the plant context estimation system.
  • context A refers to a status such that a plant requires more irrigation
  • context B indicates that the plant requires no immediate irrigation
  • context C indicates that the plant needs extra nutrient substances.
  • Calibration data in this example consists of the calibration measurements of the ground moisture and colour of a plant given the correct context. It is important that the plant context estimation system can make a clear difference between contexts A, B and C in order to maintain successful automatic plant growing greenhouse.
  • T-similarity which is based on the previously defined similarity measure.
  • T-similarity is based on the previously defined similarity measure.
  • Techniques based on T-similarity are applicable to a wide variety of data analysis problems which involve a prediction task.
  • the definition of T- similarity is as follows. Let 0 ⁇ T ⁇ 1 be a fixed real number. A context A is T- similar with a context B if a following inequality is true:
  • ad hoc-distance based methods are limited to some specific notion similarity induced by the distance metric, whereas the inventive similarity-based technique is model-based and, accordingly, is not limited to linearity or the like.
  • the methods based on T-similarity do not need any genuine test data which is not used to teach a global prediction model. Thus, all calibration data can be used to construct the model prior to its validation.
  • the data-generating mechanism is autocorrelating one
  • known split- based train-and-test techniques are not even applicable to assess realistic predictive performance of the prediction system, unlike methods based on the inventive T-simifarity measure.
  • the proposed T-similarity measure can utilize both the calibration data and the test data. Given that only calibration data is available, the inventive technique can still identify, for instance, zones that are potentially similar.
  • Figure 2 illustrates an example in which the context is determined on the basis of signal strengths in a WLAN network
  • Figure 3 illustrates how the measure similarity(A ⁇ B
  • Figure 4 shows a table which illustrates processing of signals disturbed by noise
  • Figure 5 shows an exemplary section of a WLAN network with two access points and two contexts
  • Figure 6 illustrates calibration data collected in the two contexts shown in Fig- ure 1 versus signal strength of the two base stations
  • Figure 7 is a variation of Figure 6, wherein the observed signal strengths are manipulated in order to simulate systematic blocking of the access point signals;
  • Figure 8 is a redesigned version of the network section shown in Figure 5; and Figure 9 is a diagram which shows that the redesigned version of the network section shown in Figure 8 is robust with respect to systematic blocking of the access point signals.
  • Figure 1 illustrates a general operational scheme in which the invention is used under three different contexts.
  • the invention is not limited to any particular number of contexts, however.
  • C(x) wherein x is A 1 B or C, denotes context x
  • D(x) denotes calibration data collected under the context C(x)
  • M(x) denotes the context model that is taught using the calibration data D(x). It is not necessary to use calibration data only. For instance, it is possible to im- plement the invention in such a manner that D(x) in Figure 1 represents simulated values of the explanatory quantities of interest.
  • D(x) may contain calibrated and simulated values of the explanatory values of interest which are combined statistically.
  • Illustrative but non-restrictive examples of the explanatory quantities of interest include signal strengths in WLAN network, relative humidity of air, colour of a plant and opti- cal signals under different conditions.
  • the contexts C(x), calibration data D(x) collected under the context C(x) the models M(x) taught using the calibration data D(x) are collectively denoted by reference numeral 11.
  • Reference numeral 12 denotes a data set which results from an evaluation of the swap scores.
  • Reference numeral 13 denotes a set of calculated T-similarrties.
  • FIG. 2 illustrates an example in which the context is determined based on observed signal parameter values, such as signal strengths.
  • a WLAN network comprises one or more access points, one of which is denoted by reference numeral 20.
  • This example involves determining one of three contexts A, B and C, denoted by respective reference numerals 22, 23 and 24, on the basis of the observed signal strengths.
  • Locating a receiver in a wireless communication environment per se is extensively discussed in commonly owned patent applications, some of which are listed at the end of this specification.
  • the location of the target object, or the signal strength sensing device attached to it is an example of a context
  • the signal parameter value for a specific radio transmitter, such as signal strength is an example of the context-dependent physical quantity.
  • a non-restrictive example of a context-aware application in this scenario could be an automatic lighting controlling system which controls the lighting in regions A, B, and C according to the context estimate of the target object, wherein lights are turned on only for the region indicated by the context estimate.
  • Reference numeral 25 denotes three sets of calibration data for the three contexts A, B and C.
  • Reference numeral 26 denotes a T-similarity matrix for all similarity pairs between the contexts A, B and C.
  • cell (X 1 Y) represents similarity(X-»Y
  • T-similarity is a non-restrictive example of a similarity report.
  • Figure 3 visually illustrates how the measure similarity(A ⁇ B
  • the numerical values in matrix 26 illustrate the above-mentioned fact that if context A is T-similar with context B, it does not necessarily follow that context B is T-similar with context A.
  • T-similarity threshold value 0.25 it follows that the context A is T-similar with the context B and vice versa.
  • the positioning system may mix up contexts A and B in view of the calibration data, and the user or operator should take some corrective action in order to improve the reliability of the positioning system (see the example explained in connection with Figure 2).
  • corrective action may involve adding a new access point to context A and/or B.
  • an existing access point may be relocated, as will be explained in connection with Figures 5 through 9.
  • contexts A and B may be redefined by unifying them to one context, say D 1 since they are similar to one other.
  • the environment consists of two contexts D and C that are not T-similar to one other.
  • T- similarity measures indicate that given the current model, the context C will be reliably identified, since one outlier observation of -6OdBm in the calibration data of the context C does not dominate the calibration data.
  • small calibration data sets are more vulnerable to outliers in terms of T-similarity than bigger data sets. Accordingly, if similarities are present and the calibration data sets are small the corrective action may involve collecting more calibration data.
  • contexts are so similar to each other that the context of a target object cannot be reliably estimated using a single observation made by a sensing device attached to the target object due to random variation in the observed values.
  • sequential radio signal strength observations may vary significantly even the radio receiver was static.
  • the corrective action may involve changing observing characteristics of the sensing device.
  • the sensing device may be configured to make multiple observations instead of one and provide a statistical summary of the observed values, such as an average or a median, to the context estimation system.
  • a T-similarity matrix can be used to visually identify context pairs that are likely to be mixed up with one another, prior to seeing any test data.
  • T-similarity matrix is not the same object as the commonly known confusion matrix, wherein each column of the matrix represents a count of the instances in a predicted context, while each row represents a count of the instances in an actual context.
  • the confusion matrix uses all score functions simultaneously in order to construct a global classifier.
  • time complexity of the confusion matrix is 0(n*m), where n is the number of data points to be evaluated and m is the number of contexts, e.g. possible locations.
  • Time complexity of the T-similarity matrix is lower, namely O(n), which fact makes the T-similarity matrix faster to compute. This is particularly important when parameter values of the positioning system are optimized automatically using similarity between the contexts as an optimization criterion prior to seeing any test data.
  • Another difference between the confusion matrix and the T-similarity matrix is that confusion matrix is constructed using the test data and T-similarity matrix may be constructed using calibration data only.
  • SimilaritySet(C[i]) ⁇ C[k]
  • context C[i] is T-similar with context C[k] ⁇ .
  • the similarity information can be utilized to improve robustness of a positioning system, by designing appropriate test survey paths in order to en- sure that the positioning system does not consider critical zones similar to one another.
  • It is easy to derive global measures using local T-similarities. For instance, an average size of the similarity set serves as a compact global description of the simiiarities of the positioning model. For instance, in the example illustrated in Figure 2, the average size of a similarity set would be 2/3 0,66 indicating that 66% of the contexts are vulnerable.
  • Pr[x is classified to context B
  • x is an element of the data set f(s,D(A)) > T.
  • (T,f,s)-similarity reduces to T-similarity.
  • (T,f,s)-simifarity is useful in analyzing phenomena that may arise in the actual use of the positioning system but did not occur during the calibration phase. For instance, it may occur that the signal values are over-dispersed or systematically lower for short periods of time. It is useful to know how the performance of the positioning system is affected during the short-term disorders. Performance of the positioning system can be assessed under the over- dispersion by defining the mapping f to be a random mapping such that it over- disperses the input data set via extra noise. The amount of expected noise is determined by the parameter s.
  • a and B are (T,f,s)-srmilar, which is in this case a random variable, but they are not T-similar, then it is considered that A and B are potentially similar under exceptionally noisy conditions although they are not T-similar under normal conditions. It is possible to use statistical techniques, such as Monte Carlo -integration, to estimate the following probability:
  • Pr[Pr[X is classified to context B
  • Figure 4 shows a table which illustrates addition of noise to the example shown in Figure 2.
  • the data set shown in Figure 2 was generated by repeatedly taking a sample and adding extra noise to it. In this example, these acts were repeated 10,000 times.
  • the noise was assumed additive and normally distributed with a variance of 9.
  • T-similarity values were calculated, and expectations of the T- similarity values were computed via Monte Carlo integration.
  • Reference numeral 41 denotes a data table which illustrates the corresponding expected T- similarity values.
  • FIG. 5 A section of a WLAN network is illustrated in Figure 5.
  • This exemplary section comprises two access points A and B, denoted by AP-A and AP-B, and two defined contexts C1 and C2.
  • Calibration data collected in the contexts C1 and C2 is shown in Figure 6, where the values on the x-axis and y-axis respectively represent signal strengths of the access points A and B.
  • the calibration data indicates that context C1 and C2 are not similar with one another. It is frequent in real-world positioning applica- tions, however, that for some reason signal strengths are weakened systematically.
  • FIG. 7 illustrates the corresponding manipulated calibration data sets of the contexts C1 and C2. Dashed regions, denoted by C1 and C2, represent the original high-density regions of the calibration data sets, while the two regions drawn in solid lines and denoted CV and C2 ⁇ represent the manipulated cali- bration data sets.
  • Figure 7 shows that after the systematic manipulation slightly more than half of the observations of the context C1 are classified to a wrong context C2 prior to seeing any test data. Consequently, there is a high risk that context C1 is too similar with context C2 when the signals are systematically blocked by an external object.
  • the layout of the WLAN network section can be changed.
  • Figure 8 shows an example of a changed net- work layout, wherein the access point B, denoted by AP-B, has been moved to the lower right-hand corner of the network section.
  • Figure 9 shows both the original distributions of the calibration data set values and systematically manipulated (biased) data distributions for the contexts C1 and C2 given the net- work layout shown in Figure 8.
  • Figure 9 shows that even after systematic manipulation, the contexts C1 and C2 are not mixed up with one another. This result suggests that the network layout shown in Figure 8 is robust with respect to the systematic signal blocking phenomenon.
  • Monte Carlo integration will be further discussed since it is a useful technique for some applications which are used to implement the invention, in addition to the previous examples.
  • X be an arbitrary binary random variable that has two possible realizations, say 0 and 1. Suppose it is possible to generate realizations from X's probability distribution.
  • l[.] is an indicator function that gets a value 1 whenever the input is true and value 0 otherwise. This technique is known as Monte Carlo integra- tion approximation and it has theoretical guarantees in a sense that the approximated probability converges in probability to the true probability value.
  • An exemplary implementation is as follows. In order to solve the above- described problem of false context identification, the positioning system may approximate the following probability: Pr[x is classified to context B
  • Equation (3) now obtains a computationally more tractable form given a uniform prior over the contexts Pr(A
  • x) Pr(x
  • the inventive technique can be used to speed up the process of constructing a reliable positioning model for a positioning system or to reduce resource consumption in such a model-construction building process.
  • the inven- tion also provides an opportunity to assess the noise tolerance of the system prior to applying any test data to the system.
  • the inventive technique can utilize the test data, it doesn't necessarily need any.
  • the inventive technique can also be used to automatically detect ambiguous contexts, which is beneficial in critical positioning applications. Yet further, it can be utilized in designing test case survey routes, since it identifies automatically potential error-prone regions without requiring any actual test data; actual test data may be used to verify how severe the ambiguity is.
  • the inventive technique can be used to assess and/or improve accuracy of a tracking-based positioning algorithm as follows.
  • a tracking positioning algorithm is based on a prediction of a region where the target object is moving.
  • parameters of the positioning algorithm can be adjusted in order to take into account similarity such that undesired jumps will be less likely.
  • the evolutionary process could be adjusted as follows.
  • the inventive technique can be used in dimension reduction.
  • One non- restricting example of the dimension reduction is to reduce the number of ob- servable context-dependent physical quantities used in the context models as long as the context estimation system does not have similar contexts. For example, signal strength observations for a certain radio transmitter may be omitted from calculations in case this does not decrease context estimation system performance below acceptable level.
  • Another example of reducing the number of quantities is combining two or more quantities by replacing the values of the combined quantities with a single value derived from the values of the combined quantities using some mathematical function.
  • Another non-restricting example of the dimension reduction is to reduce the number of contexts by unifying similar contexts. Benefits of the dimension reduction, in turn, include faster computation, data compression and new descriptive information regarding the problem domain.
  • the inventive technique can be used to automatically determine a reliable granularity of a context estimation system.
  • One non-restricting example is the previously described dimension reduction via context unification.
  • Another non-restricting example is to split a context to at least two new contexts. If the new contexts are not similar with each other it is an indication that a context estimation system is capable to model more detailed context features than specified in the first place.
  • the inventive technique can be used to in model selection.
  • One non- restricting example of the model selection is as follows. Assume there are three different context model types that are based on Bayesian networks, neural networks and decision trees. For each context model type T-similarities are evaluated using a context estimation system and the context model type that is the most suitable in terms of the similarities is chosen to be used in the final context estimation system.
  • the inventive method can be used to detect when the calibration model is obsolete locally vs. globally, for example by using so-called reference target devices or by collecting a new test case from time to time. If the calibration model is obsolete, then the latest data collected by the refer- ence device is not T-similar with the original local calibration data, and a new calibration effort is required either via reference devices or by manual calibra- tion. This is useful, in particular, if the positioning system is modular in the sense that local models can be updated independently of one another.
  • data derived by computer simulations such as ray-tracing techniques, may be used in constructing the statistical model instead of actually measured calibration data or in addition to it.
  • Reference documents: 1. WO2004/008795 discloses location-determination techniques which use a graph that models the topology of the target object's communication environment.
  • WO03/102622 discloses techniques for locating a target in a wireless environment.
  • the techniques use a plurality of submodels of the wireless envi- ronment, each submodel indicating a probability distribution for signal values at one or more locations in the wireless environment.
  • the submodels are combined to a probabilistic model of the environment which indicates probability distributions for signal values in the environment.
  • WO2004/008796 discloses a location-determination technique which com- prises determining a plurality of device models that compensate for the differences between different target objects' observations of signal quality parameters and selecting, among the multiple device models, a specific device model for a specific target object.
  • WO02/054813 discloses methods and equipment for estimating a re- DCver's location in a wireless telecommunication environment.
  • Finnish patent application FI20055649 discloses a method and system for estimating a target object's properties, including location, in an environment.
  • a topology model indicates permissible locations and transitions and a data model models a location-dependent physical quantity which is observed by the target object's co-located sensing device.
  • Motion models model specific target object types, obeying the permissible locations and transitions.
  • the target object is assigned a set of particles, each having a set of attributes, including location in relation to the topology model.
  • the attributes estimate the target object properties.
  • the particles' update cycles comprise: determining a degree of belief for each particle to estimate the target object properties; determining a weight for each particle based on at least the determined degree of belief and generating new particles for update cycle n+1 in an evolutionary process.

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  • Complex Calculations (AREA)

Abstract

L'invention concerne un système d'évaluation de contexte, destiné à observer des quantités physiques sur un site objet cible (21) et à produire une évaluation de contexte, utilisée pour déclencher des actions contextuelles. Chaque contexte (22-24) définit des circonstances concernant l'observation des quantités physiques. Des données d'étalonnage (25), établies dans ces circonstances, sont utilisées pour construire des modèles de contexte. Les modèles de contexte sont utilisés par une fonction de notation, qui indique une probabilité pour qu'une observation donnée soit obtenue dans un contexte donné. Des similarités entre deux modèles de contexte sont identifiées en procédant aux étapes consistant à sélectionner deux contextes, sélectionner une observation contenue dans les données d'étalonnage du premier contexte; calculer une première note avec l'observation sélectionnée est le premier contexte; calculer une seconde note avec l'observation sélectionnée et le second contexte; et mettre à jour la similarité en utilisant la première et la seconde note. La similarité peut être utilisée pour procéder à des changements qui influent sur les performances du système d'évaluation de contexte.
PCT/FI2007/050651 2006-11-30 2007-11-30 Techniques d'amélioration de la fiabilité d'un système de prédiction Ceased WO2008065257A1 (fr)

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US9646454B1 (en) 2001-02-06 2017-05-09 Nexrf Corp Networked gaming system and method
US9373116B1 (en) 2001-07-05 2016-06-21 NexRf Corporation Player tracking using a wireless device for a casino property
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US9406079B1 (en) 2006-11-30 2016-08-02 NexRf Corporation Content relevance weighting system
US10560798B2 (en) 2006-11-30 2020-02-11 Nexrf, Corp. Targeted content delivery
US10430492B1 (en) 2006-11-30 2019-10-01 Nexrf, Corp. System and method for handset positioning with dynamically updated RF fingerprinting
US11729576B2 (en) 2008-03-29 2023-08-15 NEXRF Corp. Targeted content delivery
US8738024B1 (en) 2008-03-29 2014-05-27 Nexrf, Corp. Delivering content within a boundary with beacons
US10721705B1 (en) 2010-06-04 2020-07-21 NEXRF Corp. Content Relevance Weighting System
US10657793B2 (en) 2013-04-18 2020-05-19 Airista Flow, Inc. Processing alert signals from positioning devices
US10152874B2 (en) 2013-04-18 2018-12-11 Airista Flow, Inc. Processing alert signals from positioning devices
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US12183183B2 (en) 2013-04-18 2024-12-31 Airista Flow, Inc. Processing alert signals from positioning devices
US10503912B1 (en) 2014-08-12 2019-12-10 NEXRF Corp. Multi-channel communication of data files
US11550930B2 (en) 2014-08-12 2023-01-10 NEXRF Corp. Multi-channel communication of data files
US9788155B1 (en) 2015-04-22 2017-10-10 Michael A. Kerr User interface for geofence associated content
US10838582B2 (en) 2016-06-15 2020-11-17 NEXRF Corp. Mobile autonomous dynamic graphical user interface

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