WO2020208987A1 - Position estimation device, position estimation method, and program - Google Patents
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- WO2020208987A1 WO2020208987A1 PCT/JP2020/009924 JP2020009924W WO2020208987A1 WO 2020208987 A1 WO2020208987 A1 WO 2020208987A1 JP 2020009924 W JP2020009924 W JP 2020009924W WO 2020208987 A1 WO2020208987 A1 WO 2020208987A1
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Definitions
- the present invention relates to a position estimation device, a position estimation method and a program.
- the existing source position estimation system includes a receiving means, a signal processing means, a storage means, and a position estimating means.
- the receiving means receives radio waves from the source.
- the receiving means are arranged at different positions from each other.
- the signal processing means acquires signals transmitted from each of the plurality of candidate positions and performs signal processing.
- the storage means holds a set of features obtained as a result of signal processing by the signal processing means.
- the position estimation means compares the set of features obtained by measuring the signal transmitted from the position to be estimated by the receiving means with the set of features held by the storage means to determine the position of the source. presume.
- the existing source position estimation system having the above configuration operates in the following two steps.
- the first step is the "training" step.
- radio waves are transmitted from known positions that are candidates for source positions, and are received by receiving means arranged at different known positions.
- the signal processing means performs signal processing on the received signal to acquire the feature amount.
- a set of features equal to the number of receiving means is also called a position fingerprint with respect to its source position.
- the training data is acquired for a plurality of positions that are candidates for the source position and is stored in the storage means.
- the second step is the "estimation" step.
- a position fingerprint is acquired for a source whose position is unknown by the same procedure as in the training stage. Then, among the position fingerprints created in advance during training, the position corresponding to the position fingerprint closest to the position fingerprint to be estimated is treated as the estimated position of the source.
- Patent Document 1 discloses that reception strength (RSS; Received Signal Strength) and channel impulse response (CIR; Channel Impulse Response) are used as feature quantities.
- RSS Received Signal Strength
- CIR Channel Impulse Response
- the document also states that by interpolating the center frequency, bandwidth, and position of the transmitted signal with respect to the CIR data set, a signal different from the transmitted signal used during training is transmitted.
- a method for expanding the position fingerprint data so that it can be estimated is disclosed.
- As an algorithm for performing interpolation for a position there are a method using kriging in addition to linear, solid, and spline interpolation.
- Patent Document 1 An existing source position estimation system as disclosed in Patent Document 1 has a problem that it is necessary to acquire position fingerprints for a sufficient number of candidate positions at the training stage when interpolating positions. There is.
- the reason is that if the number of candidate positions is not sufficient, it may not be possible to synthesize the position fingerprints corresponding to the sources located in between by simply superimposing the position fingerprints for each candidate position. For example, a position fingerprint that is acquired when radio waves are simultaneously transmitted from each candidate position may be synthesized.
- a main object of the present invention is to provide a position estimation device, a position estimation method, and a program that contribute to reducing the number of position fingerprints acquired in the training stage.
- a prediction model generation that predicts a feature amount depending on a transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
- a unit a measured value of a feature amount acquired based on a signal from a reference source at a known position, and a predicted value of a feature amount related to the reference source at a known position calculated using the prediction model.
- the error group is calculated according to the positional relationship between the error group calculation unit, the pseudo source at an arbitrary position, and each of the reference sources, which calculates the error group by calculating the error for a plurality of reference source positions.
- Pseudo-measured value correction that uses the synthesis error to correct the predicted value of the feature amount by the prediction model when the signal from the pseudo source and the error synthesis unit, which synthesizes and outputs as a synthesis error, is received.
- the source position is estimated by inputting the measured value of the feature amount depending on the unit and the transmission position into the learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
- a position estimation device including a position estimation unit is provided.
- a prediction model for predicting a feature amount depending on the transmission position obtained when a signal from a source at an arbitrary position is received at a specific position is generated. Steps to be performed, the measured value of the feature amount acquired based on the signal from the reference source at the known position, and the predicted value of the feature amount related to the reference source at the known position calculated using the prediction model.
- the error group is calculated by calculating the error of the above for a plurality of reference source positions, and the error group is synthesized according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources.
- a position estimation method including a step of estimating the source position by inputting the measured value of the above into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. Will be done.
- the computer mounted on the position estimation device predicts the feature amount depending on the transmission position obtained when a signal from a source at an arbitrary position is received at a specific position.
- the error group is calculated by calculating the error between the predicted value and the reference source positions for a plurality of reference source positions, and the error is calculated according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources.
- a position estimation device a position estimation method, and a program that contribute to reducing the number of position fingerprints acquired in the training stage are provided. According to the present invention, other effects may be produced in place of or in combination with the effect.
- FIG. 1 is a diagram for explaining an outline of one embodiment.
- FIG. 2 is a diagram showing an example of a schematic configuration of a source position estimation system according to the first embodiment.
- FIG. 3 is a diagram for explaining the operation of the source position estimation system according to the first embodiment.
- FIG. 4 is a diagram showing an example of a processing configuration (processing module) of the position estimation device according to the first embodiment.
- FIG. 5 is a flowchart showing an example of the operation of the training stage of the source position estimation system according to the first embodiment.
- FIG. 6 is a diagram for explaining the concept of an RSS measurement value set.
- FIG. 7 is a flowchart showing an example of an operation of creating training data by a reference source.
- FIG. 1 is a diagram for explaining an outline of one embodiment.
- FIG. 2 is a diagram showing an example of a schematic configuration of a source position estimation system according to the first embodiment.
- FIG. 3 is a diagram for explaining the operation of the source position estimation system according to the first embodiment
- FIG. 8 is a diagram for explaining the operation of the propagation model generation unit according to the first embodiment.
- FIG. 9 is a diagram for explaining the operation of the reference source error set creating unit according to the first embodiment.
- FIG. 10 is a flowchart showing an example of an operation of creating training data by a pseudo source.
- FIG. 11 is a diagram for explaining the operation of the error set synthesis unit for the pseudo source according to the first embodiment.
- FIG. 12 is a diagram for explaining the operation of the pseudo measurement value set correction unit according to the first embodiment.
- FIG. 13 is a flowchart showing an example of the operation of the estimation stage of the source position estimation system according to the first embodiment.
- FIG. 14 is a diagram showing an example of a processing configuration (processing module) of the position estimation device according to the second embodiment.
- FIG. 15 is a diagram showing an example of a processing configuration of the source position estimation system according to the third embodiment.
- FIG. 16 is a diagram showing an example of a processing configuration of the source position estimation system according to the fourth embodiment.
- FIG. 17 is a diagram showing an example of a processing configuration of the source position estimation system according to the fifth embodiment.
- FIG. 18 is a diagram showing an example of the hardware configuration of the position estimation device.
- the position estimation device 100 includes a prediction model generation unit 101, an error group calculation unit 102, an error synthesis unit 103, a pseudo measurement value correction unit 104, and a position estimation unit 105 (FIG. 1). reference).
- the prediction model generation unit 101 generates a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
- the error group calculation unit 102 includes a measured value of the feature amount acquired based on the signal from the reference source at the known position, and a predicted value of the feature amount related to the reference source at the known position calculated using the prediction model.
- the error group is calculated by calculating the error of, for a plurality of reference source positions.
- the error synthesis unit 103 synthesizes an error group according to the positional relationship between the pseudo transmission source at an arbitrary position and each of the reference transmission sources, and outputs it as a synthesis error.
- the pseudo-measured value correction unit 104 corrects the predicted value of the feature amount by the prediction model when the signal from the pseudo-source is received by using the synthesis error.
- the position estimation unit 105 estimates the source position by inputting the measured value of the feature amount depending on the transmission position into the learning model learned at least based on the predicted value of the feature amount from the corrected pseudo source. To do.
- the position estimation device 100 receives, for example, a transmitted radio wave by sensors arranged around it, and estimates the source position using a feature amount depending on the transmission position obtained from the received waveform.
- the position estimation device 100 does not simply superimpose the position fingerprints corresponding to the plurality of candidate positions, but superimposes the spatial distribution of the error between the measured value and the predicted value at the plurality of candidate positions, and the space of the error of the predicted value. Correct the predicted value using the distribution. That is, the position estimation device 100 takes into consideration the usual tendency that the feature amount (for example, RSS) becomes smaller as the distance from the source is increased, so that the position fingerprints at a small number of candidate positions can be moved to an arbitrary transmission position. Create a corresponding position fingerprint. The created position fingerprint expands the training data based on the measured values. As a result, the number of position fingerprints actually measured and acquired at the training stage can be reduced.
- the feature amount for example, RSS
- the position fingerprint corresponding to the transmission position outside the candidate position for which the position fingerprint was acquired in the training stage is accurately created.
- the degree of deterioration of the estimation accuracy can be reduced.
- FIG. 2 is a diagram showing an example of a schematic configuration of a source position estimation system according to the first embodiment.
- the source position estimation system includes a plurality of receiving devices 10-1 to 10-M, a plurality of signal processing devices 20-1 to 20-M, a storage device 30, and a position estimating device 40. And are included (M is a positive integer, the same applies hereinafter).
- M is a positive integer, the same applies hereinafter.
- receiving device 10 if there is no particular reason for distinguishing the receiving devices 10-1 to 10-M, it is simply referred to as "receiving device 10". Similarly, unless there is a particular reason for distinguishing the signal processing devices 20-1 to 20-M, the term "signal processing device 20" is simply used.
- the receiving device 10 is arranged in the field to be the target of the source position estimation.
- the receiving device 10 is exemplified by a radio wave sensor or the like that receives radio waves.
- the signal processing device 20 is a device that performs signal processing using the signal received by the receiving device 10. Specifically, the signal processing device 20 calculates a feature amount (for example, reception intensity RSS) from a received signal (radio signal).
- a feature amount for example, reception intensity RSS
- the storage device 30 stores the position of the receiving device 10 in association with the feature amount calculated by the signal processing device 20.
- the position estimation device 40 estimates the position in the field of a signal source whose position is unknown based on the information stored in the storage device 30.
- the position estimation device 40 estimates the position of the source in the field as shown in FIG. In FIG. 3, the radio wave sensor corresponds to the receiving device 10.
- the position estimation device 40 generates a feature amount map (for example, a radio wave intensity map; position fingerprint) for each position of the reference source, and collates the map with the feature amount map (position fingerprint) obtained from an unknown source. , Estimate the location of an unknown source.
- a feature amount map for example, a radio wave intensity map; position fingerprint
- FIG. 4 is a diagram showing an example of a processing configuration (processing module) of the position estimation device 40 according to the first embodiment.
- the position estimation device 40 includes an input unit 201, a data classification unit 202, a training data expansion unit 203, a training data combination unit 204, a learning unit 205, a position estimation unit 206, and an output unit. 207 and.
- the input unit 201 inputs an RSS measurement value set when a signal from a source at an arbitrary position is received at a specific position.
- the data classification unit 202 classifies the input data as training data or estimation data.
- the training data expansion unit 203 generates pseudo data (training data) based on the training data.
- the training data combination unit 204 combines the training data obtained by actually measuring and the training data generated in a pseudo manner.
- the training data combining unit 204 generates the above two training data (training data based on actual measurement values, training data generated in a pseudo manner) as training data for training.
- the learning unit 205 learns the correspondence between the RSS measurement value set and the correct position based on the combined training data. More specifically, the learning unit 205 has a measured value of the feature amount acquired based on the signal from the reference source (training data based on the measured value) and a predicted value of the feature amount from the pseudo source (pseudo source). Based on the training data), the above correspondence is learned.
- the position estimation unit 206 estimates the source position corresponding to the input estimation data based on the result learned by the learning unit 205.
- the position estimation unit 206 is a learning model (learning model generated by the learning unit 205) in which the measured value of the feature amount depending on the transmission position is learned based on at least the predicted value of the feature amount from the corrected pseudo source.
- the source position is estimated by inputting to.
- the output unit 207 outputs the estimated source position.
- RSS is an example of a feature amount depending on the transmission position.
- a set of RSS measurement values [RSS 1 , RSS 2 , ..., RSS M ] measured at M different positions is an example of a position fingerprint.
- RSS measurement value set consisting of 9 RSSs is calculated.
- N N is a positive integer.
- the suffix i is 1 to N.
- RSS is an example of the feature amount that can be used in the disclosure of the present application, and as another feature amount, CIR or the difference between the reference time and the arrival time may be used.
- the input unit 201 may include preprocessing accompanied by monotonic mapping such as mutual conversion between linear units and logarithmic units of RSS measurement values, scaling such as normalization and standardization.
- the data classification unit 202 distributes the data acquired from the input unit 201 into “training data” or “estimation data” according to the mode of the source position estimation system.
- the mode of the source position estimation system is either “training mode” or “estimation mode", and the system administrator or the like determines and inputs the mode of the source position estimation system.
- the training data expansion unit 203 includes a propagation model generation unit 211, a reference source error set creation unit 212, a pseudo source position determination unit 213, a model-based pseudo measurement value set generation unit 214, and a pseudo source error set. It includes a synthesis unit 215 and a pseudo measurement value set correction unit 216.
- the propagation model generation unit 211 generates a radio wave propagation model using the training data.
- the propagation model generation unit 211 obtains a feature amount (for example, radio field intensity) depending on the transmission position, which is obtained when the receiving device 10 receives a signal from a transmission source (reference transmission source) at an arbitrary position at a specific position. Generate a model to predict. The details of the prediction model will be described later.
- the reference source error set creation unit 212 creates a set of errors between the RSS pseudo-measurement value set predicted from the generated propagation model and the RSS measurement value set actually obtained.
- the reference source error set creation unit 212 uses the measured value (actual measurement value) of the feature amount acquired based on the signal from the reference source at the known position and the reference transmission at the known position calculated using the above prediction model. Calculate the predicted value of the feature quantity for the source and the error.
- the reference source error set creation unit 212 calculates an error group (plurality of error maps) by executing the above error calculation for a plurality of reference source positions.
- the pseudo source position determination unit 213 determines the position of the pseudo source provided for training data expansion.
- the model-based pseudo measurement value set generation unit 214 generates a pseudo RSS measurement value set based on the pseudo source position and the propagation model.
- the error set synthesis unit 215 for the pseudo source synthesizes an error group (plurality of error maps) according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources, and outputs it as a composite error.
- the error set synthesis unit 215 for a pseudo source synthesizes an error set based on the degree of spatial correlation between the pseudo source position and the reference source position, and synthesizes the error set for the pseudo source.
- the pseudo measurement value set correction unit 216 corrects the generated model-based pseudo measurement value set with the combined error set for the pseudo source and outputs it.
- the pseudo measurement value set correction unit 216 corrects the predicted value of the feature amount by the prediction model when the signal from the pseudo transmission source is received by using the above synthesis error.
- the propagation model generation unit 211 is an example of the prediction model generation unit.
- the reference source error set creation unit 212 is an example of an error group calculation unit.
- the error set synthesis unit 215 for a pseudo transmission source is an example of an error synthesis unit.
- FIG. 5 is a flowchart showing an example of the operation of the training stage of the source position estimation system according to the first embodiment. First, the operation of the training stage will be described in detail with reference to the flowchart of FIG.
- Step S31 is a preparatory step for the training stage.
- the source position estimation system receives a signal from the source (reference source) and generates a set of position coordinates generated.
- the reception position coordinates may be input to the system as a distance from the reference point using a laser range finder or the like, or may be input by using a satellite positioning system or the like. It is desirable to set the reception position so that radio waves can be received at a plurality of locations when the transmission source is located at an arbitrary position in the area. For example, in the example of FIG. 3, the position where the nine radio wave sensors are arranged is input to the system.
- the source position estimation system creates training data using a reference source whose position is known (step S32).
- the training data is a set of multiple reference source positions and RSS measurement value sets obtained correspondingly.
- the position of the reference transmission source is changed, and the received signal is collected from the radio wave sensor (reception device 10) at each position.
- a feature amount for example, RSS
- RSS is calculated from the received signal for each position of the reference source, and an RSS measurement value set (position fingerprint) is obtained.
- FIG. 6 is a diagram for explaining the concept of the RSS measurement value set.
- the radio wave sensor receives the radio wave transmitted from the reference source, and the information relating the position of each radio wave sensor and the feature amount obtained from the sensor is the RSS measurement value set (position). Fingerprint).
- the information in which the position fingerprint is associated with the position of the reference source is the training data.
- the source position estimation system creates training data by a pseudo source based on the training data obtained by actually measuring (step S33). Details regarding the creation of training data by the pseudo source will be described later.
- the source position estimation system uses the training data created by combining the training data created in step S32 and the training data created by the pseudo source created in step S33 to obtain a position estimator.
- Train (step S34) That is, the source position estimation system generates a learning model using two training data (training data by an actual reference source and training data by a pseudo source).
- FIG. 7 is a flowchart showing an example of an operation of creating training data by the reference source in step S32. The operation of step S32 will be described in detail with reference to FIG. 7.
- the radio waves transmitted by the reference source are received by the receiving device 10 at a plurality of positions, and the RSS is measured by the signal processing device 20 (step S41).
- the source position estimation system acquires the position of the reference source (step S42).
- a set of a plurality of RSS measurement values obtained in step S41 is a position fingerprint.
- the number of reference transmission sources is about the same as the number of reception positions to about 5 times as many. If it is less than that, the accuracy when generating the training data by the pseudo source from the training data by the reference source deteriorates, and high position accuracy cannot be obtained. Further, if it is more than that, it is desirable from the viewpoint of improving the position estimation accuracy, but the effect of reducing the amount of data required at the training stage is reduced.
- the propagation model generation unit 211 of the training data expansion unit 203 creates (estimates) a propagation model based on the reference source position and the actual RSS measurement value set (step S43). Specifically, the propagation model generation unit 211 models how much RSS is attenuated according to the distance between the source and the reception position.
- FIG. 8 is a diagram for explaining the operation of the propagation model generation unit 211.
- the propagation model generation unit 211 sets the position of the reference source as the center (origin), sets the distance between the position of the reference source whose position is known and the radio wave sensor (receiver 10) on the X-axis, and each radio wave.
- the radio field intensity in the sensor is set on the Y-axis, and a data set (graph) is generated.
- the propagation model generation unit 211 performs regression analysis on the data set to generate a propagation model. In the example of FIG. 8, a straight line is generated as a propagation model.
- the propagation model generation unit 211 calculates a constant (propagation model constant) representing the straight line shown in FIG.
- the reference source error set creation unit 212 calculates the predicted RSS value at each reception position based on the propagation model created above (step S44).
- FIG. 9 is a diagram for explaining the operation of the reference source error set creation unit 212.
- the reference source error set creation unit 212 generates a radio field intensity map as shown in the lower left of FIG.
- the reference source error set creation unit 212 inputs the distance between the reference source and the radio wave sensor (receiver 10) into the propagation model estimated in step S43, and calculates the radio wave intensity at the radio wave sensor position.
- the reference source error set creation unit 212 calculates the radio wave intensity of each radio wave sensor arranged in the field, and calculates a radio wave intensity map (RSS predicted value) as shown in the lower left of FIG.
- RSS predicted value radio wave intensity map
- the reference source error set creation unit 212 calculates the difference between the actual RSS measurement value and the predicted value calculated in step S44, and saves it as an error set (step S45).
- the difference between the upper left radio field intensity map (actual RSS measurement value) and the lower left radio wave intensity map (RSS predicted value) is calculated, and the error map of the radio wave intensity for each reference source position is calculated. ..
- the radio field intensity map generated by the propagation model described in the lower left of the drawing is a predicted value that is not affected by obstacles existing in the actual field, and therefore the radio field strength increases as the distance from the source increases. Will be low.
- the radio field intensity map on the upper left of the drawing is an actually measured value, it is affected by obstacles existing in the field.
- the influence of obstacles or the like at each reference source position is calculated as an error.
- the shading related to the error of the radio field intensity shown in FIG. 9 and the like indicates that the center color has a small error, and the edge color has a large error (positive error, negative error).
- FIG. 10 is a flowchart showing an example of an operation of creating training data by the pseudo source in step S33. The operation of step S33 will be described in detail with reference to FIG.
- the pseudo source position determination unit 213 sets the position of the pseudo source (step S51).
- the pseudo source position determination unit 213 sets the position of the pseudo source within a region where an unknown source, which is a position estimation target, can exist. At that time, it is desirable that the pseudo source position determination unit 213 sets a position different from the position where the reference source already exists as the pseudo source position.
- the pseudo source position determination unit 213 sets the pseudo source at a plurality of positions so as to cover the entire area.
- the number of pseudo-sources is preferably about 5 to 10 times the number of reference sources. If it is less than that, it may not be possible to cover the entire region with sufficient density, and if it is more than that, the amount of calculation will increase and the improvement in accuracy will be limited.
- the model-based pseudo-measurement value set generation unit 214 pseudo-measures the RSS measurement value set obtained when the radio wave transmitted from the pseudo-source position is received at each reception position based on the propagation model generated in step S43. Calculated as a value set (step S52).
- the model-based pseudo-measurement value set generator 214 inputs the difference between the position of the pseudo-source and the position of the radio wave sensor into the propagation model of the reference source closest to the position of the selected pseudo-source, and RSS for each pseudo-source. Generate a measurement value set (radio field intensity map).
- the RSS measurement value set for each pseudo source may be generated from a propagation model that is commonly used for all pseudo sources.
- the propagation model generation unit 211 plots the data for all the reference sources as shown in FIG. 8, and the propagation model commonly used for all the pseudo sources from the plotted data. To generate.
- the RSS measurement value set for each pseudo source may be generated from a propagation model obtained by weighted averaging with a weighting coefficient determined according to the distance between the pseudo source position and the reference source. ..
- the method for generating the propagation model in this case can be the same as the method for generating the error set by the error set synthesis unit 215 for the pseudo source, which will be described later.
- the error set synthesis unit 215 for the pseudo source synthesizes the error set for each reference source position saved in step S45, and creates an error set according to the pseudo source position (step S53). For example, the error set synthesis unit 215 for a pseudo source weights and averages the error set with a predetermined weighting coefficient based on the relative relationship between the reference source source position and the pseudo source position (positional relationship between the two sources). By doing so, the error set corresponding to the pseudo source position is synthesized.
- FIG. 11 is a diagram for explaining the operation of the error set synthesis unit 215 for a pseudo source.
- the error set synthesis unit 215 for the pseudo source synthesizes (adds the radio field intensity) the error set of each reference source position calculated earlier.
- the error set synthesis unit 215 for the pseudo source synthesizes the error set by the weighted average reflecting the position of the pseudo source and the position of the reference source. For example, in the upper left corner of FIG. 11, since the distance between the reference source and the pseudo source is long, the weight of the reference source is reduced. On the other hand, in the lowermost row on the left side, the distance between the reference source and the pseudo source is short, so the weight of the reference source is increased.
- an error error of radio field intensity
- the error set synthesis unit 215 for the pseudo source generates an error map of the pseudo source in consideration of the positions of the above two types of sources.
- a weighting coefficient proportional to the reciprocal of the distance between the reference source position and the pseudo source position is used.
- a weighting factor proportional to the -1.5th power or -2nd power of the distance may be used.
- the error set synthesis unit 215 for a pseudo source sets the similarity of each error set to the distance between two points for any two reference sources. Create a spatial correlation model of how it changes accordingly. Then, the error set synthesis unit 215 for the pseudo source determines the weighting coefficient according to the distance between the reference source position and the pseudo source position so as to be consistent with the spatial correlation model. As described above, there is kriging as a method of determining the weighting coefficient in consideration of the spatial correlation.
- the pseudo measurement value set correction unit 216 corrects the pseudo measurement value set generated in step S52 by the error set created in step S53 (step S54). Specifically, the pseudo measurement value set correction unit 216 corrects the pseudo measurement value set generated in step S52 by adding the pseudo measurement value set and the error set for each pseudo transmission position.
- FIG. 12 is a diagram for explaining the operation of the pseudo measurement value set correction unit 216.
- the pseudo measurement value set correction unit 216 adds the radio field intensity of the pseudo measurement value set of the pseudo source position and its error, a radio field intensity map at the corrected pseudo source position as shown on the right side of FIG. 12 is obtained. Be done. The radio wave intensity map becomes pseudo position fingerprint data.
- FIG. 13 is a flowchart showing an example of the operation of the estimation stage of the source position estimation system according to the first embodiment. The operation of the estimation stage will be described in detail with reference to the flowchart of FIG.
- the reception position is acquired in the same manner as in step S31 (step S61).
- the input unit 201 acquires the RSS measurement value set calculated by the signal processing means in the same manner as in step S41 (step S62).
- the position estimation unit 206 collates the RSS measurement value set obtained for the unknown source with the RSS measurement value set included in the training data by using the position estimator trained in step S34. (Step S63).
- the output unit 207 outputs the source position corresponding to the RSS measurement value set of the nearest training data as the estimated position of the unknown source (step S64).
- a case where a multi-class classification method of selecting and outputting one of the closest candidate positions from a large number of candidate positions is used has been described.
- a regression analysis method may be used in which the position coordinates of the source are treated as continuous numerical values and output.
- a weighted coefficient of similarity is used for a plurality of candidate positions corresponding to the RSS measurement value set obtained for an unknown source and the RSS measurement value set of a plurality of training data having a large degree of similarity.
- the average may be calculated and treated as an estimated position.
- Examples of multi-class classification methods include logistic regression, random forest, support vector machine, decision tree, neural network, k-nearest neighbor method, etc.
- Examples of regression analysis methods include linear regression, logistic regression, support vector machines, random forests, and neural networks.
- the position estimation method to which the disclosure of the present application is applicable is not limited to the above examples.
- the training data is expanded by creating the training data by the pseudo source based on the training data by the reference source.
- the number of RSS measurement value sets that are actually measured and acquired can be reduced.
- the error sets of the measured values and the predicted values for the plurality of reference source positions are superimposed, and further, the error sets are superimposed.
- the predicted value is corrected using. Therefore, it is possible to avoid synthesizing RSS measurement value sets in which radio waves are simultaneously transmitted from a plurality of candidate positions. That is, it is rare that radio waves are transmitted from a plurality of candidate positions at the same time during system operation, and even if a learning model is generated using such a rare RSS measurement value set, the estimation result by the learning model is obtained. The accuracy will be low.
- a propagation model is created based on the RSS measurement value set actually measured using the reference source. Therefore, the RSS measurement value set obtained when the pseudo source is placed outside the reference source position is accurately created. As a result, the degree of deterioration of the estimation accuracy is reduced even when an unknown source exists outside the reference source position in the training stage.
- FIG. 14 is a diagram showing an example of a processing configuration (processing module) of the position estimation device 40 according to the second embodiment.
- the position estimation device 40 according to the second embodiment is different from the position estimation device 40 according to the first embodiment in that it further has a plurality of wave source pseudo measurement value set synthesis unit 701. .
- the plurality of wave source pseudo measurement value set synthesis unit 701 is an example of the pseudo measurement value synthesis unit.
- the multi-wave source pseudo-measured value set synthesis unit 701 operates as follows.
- an RSS measurement value set when the number of wave sources is 1.
- the RSS measurement value set to be superimposed may be either an RSS measurement value set by a reference source or an RSS measurement value set by a pseudo source. Further, in the case of RSS, it is desirable to add the power level in a linear representation as the superposition method. However, monotonic mapping processing such as conversion to logarithmic representation after addition may be performed.
- one RSS measurement value set corresponds to one or more source positions. Therefore, the position estimation unit 206 needs to be configured so that it can output one or more source positions. For example, a multi-label classification method can be used in which a plurality of candidate positions are selected and output for one RSS measurement value set.
- FIG. 15 is a diagram showing an example of a processing configuration of the source position estimation system according to the third embodiment.
- M sensors 801-1 to 801-M that receive signals from the source are used. It differs from the communication network 802 in that it has a communication unit 803.
- Each of the plurality of sensors 801-1 to 801-M includes a radio wave intensity measuring unit 804, a position measuring unit 805, and a communication unit 806 for transmitting the measured data.
- the radio wave intensity measuring unit 804 is an example of a measuring unit that measures a feature amount depending on the transmission position.
- the radio wave intensity measuring unit 804 receives the radio wave from the source and measures its RSS.
- the position measurement unit 805 measures its own position.
- the communication unit 806 transmits the measured data to the communication unit 803 via the communication network 802.
- the sensors 801-1 to 801-M measure the RSS and the self-position, and the measured data is connected to the data classification unit 202 via the communication network 802. , Training and estimation process can be carried out online.
- FIG. 16 is a diagram showing an example of a processing configuration of the source position estimation system according to the fourth embodiment.
- the fourth embodiment is different in that it has a sensor position holding unit 901 and a sensor position adding unit 902 instead of the position measuring unit 805 of the third embodiment.
- the sensor position holding unit 901 holds the position of each sensor installed in the area.
- the sensor position addition unit 902 adds the position of each sensor to the data measured by the sensors 801-1 to 801-M obtained via the communication unit 803 and outputs the data.
- the sensor position held by the sensor position holding unit 901 is added to the RSS measurement value set measured by the sensors 801-1 to 801-M so as to be connected to the data classification unit 202. ing. Therefore, the position measurement unit 805 of the sensor becomes unnecessary, and the sensor can be manufactured at low cost.
- FIG. 17 is a diagram showing an example of a processing configuration of the source position estimation system according to the fifth embodiment.
- the fifth embodiment is different from the fourth embodiment in that it has a time / weather measurement unit 1001 and a time / weather label addition unit 1002.
- the time / weather measurement unit 1001 acquires calendar information such as the date and time of measurement, the day of the week, and public holidays, as well as the weather, temperature, and humidity of that day.
- the time / weather label addition unit 1002 adds time information and weather information to the data measured by the sensors 801-1 to 801-M obtained via the communication unit 803 and outputs the data.
- the data classification unit 202 can classify according to time and weather. That is, training and estimation can be performed according to the assigned time label and weather label. For example, with regard to time, since the vegetation changes according to the season with a cycle of one year, radio waves are easily absorbed and attenuated by the vegetation in the summer when the leaves are overgrown, and the radio waves are blocked in the winter when the leaves fall. It disappears and is easy to reach long distances.
- the sensors 801-1 to 801-M may have the time / weather measurement unit 1001.
- FIG. 18 is a diagram showing an example of the hardware configuration of the position estimation device 40.
- the position estimation device 40 can be configured by an information processing device (so-called computer), and includes the configuration illustrated in FIG.
- the position estimation device 40 includes a processor 311, a memory 312, an input / output interface 313, a communication interface 314, and the like.
- the components such as the processor 311 are connected by an internal bus or the like so that they can communicate with each other.
- the position estimation device 40 may include hardware (not shown), or may not include an input / output interface 313 if necessary. Further, the number of processors 311 and the like included in the position estimation device 40 is not limited to the example of FIG. 18, and for example, a plurality of processors 311 may be included in the position estimation device 40.
- the processor 311 is a programmable device such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a DSP (Digital Signal Processor). Alternatively, the processor 311 may be a device such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The processor 311 executes various programs including an operating system (OS; Operating System).
- OS Operating System
- the memory 312 is a RAM (RandomAccessMemory), a ROM (ReadOnlyMemory), an HDD (HardDiskDrive), an SSD (SolidStateDrive), or the like.
- the memory 312 stores an OS program, an application program, and various data.
- the input / output interface 313 is an interface of a display device or an input device (not shown).
- the display device is, for example, a liquid crystal display or the like.
- the input device is, for example, a device that accepts user operations such as a keyboard and a mouse.
- the communication interface 314 is a circuit, module, or the like that communicates with another device.
- the communication interface 314 includes a NIC (Network Interface Card) and the like.
- the function of the position estimation device 40 is realized by various processing modules.
- the processing module is realized, for example, by the processor 311 executing a program stored in the memory 312.
- the program can also be recorded on a computer-readable storage medium.
- the storage medium may be a non-transient such as a semiconductor memory, a hard disk, a magnetic recording medium, or an optical recording medium. That is, the present invention can also be embodied as a computer program product.
- the program can be downloaded via a network or updated using a storage medium in which the program is stored.
- the processing module may be realized by a semiconductor chip.
- the present invention is suitable for applications such as estimating the position of an illegal / illegal radio wave transmission source and taking appropriate measures. It is also suitable for applications such as detecting and tracking the position of a person or object equipped with a radio wave transmitter (beacon).
- Prediction model generation units (101, 211) that generate a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position. Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model.
- a position estimation unit that estimates the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. (105, 206) and Position estimation devices (40, 100).
- the error synthesizing unit (103, 215) weighted and averages the error group using a weighting coefficient determined according to the positional relationship between the pseudo source at the arbitrary position and each of the reference sources, and the combined error.
- the position estimation device (40, 100) according to Appendix 1 for calculating the above.
- [Appendix 3] Based on the measured value of the feature amount acquired based on the signal from the reference source of the known position and the predicted value of the feature amount from the pseudo source after the correction, the feature amount and the correct answer position depending on the transmission position
- the position estimation device (40, 100) according to Appendix 1 or 2 further comprising a learning unit (205) that learns correspondences and generates a learning model.
- Appendix 6 In any one of Appendix 1 to 5, further including a pseudo-measurement value synthesizer (701) that synthesizes and outputs one or more of the measurement values corresponding to different source positions or the pseudo-measurement values by the pseudo-source.
- Appendix 7 The position estimation device (40) according to any one of Supplementary note 1 to 6, further comprising a sensor including a measurement unit for measuring a feature amount depending on the transmission position and a communication unit (803) connected via a network. , 100).
- Appendix 8 The position estimation device (40, 100) according to any one of Supplementary notes 1 to 7, wherein the feature amount is a radio wave intensity.
- the time / weather measurement unit (1001) that acquires time information related to the time when the measurement was performed or weather information related to the weather on the day when the measurement was performed.
- a time / weather label addition unit (1002) that adds the time information or the weather information to the data measured by the sensor obtained via the communication unit.
- the position estimation device (40, 100) according to Appendix 7, further comprising.
- the step of calculating the error group by calculating for the reference source position of A step of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
- Location estimation methods including.
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Abstract
[Problem] To provide a position estimation device that reduces the number of location fingerprints to be acquired in a training stage. [Solution] This position estimation device comprises a prediction model generation unit, error group calculation unit, error combination unit, pseudo measurement value correction unit, and position estimation unit. The prediction model generation unit generates a prediction model for predicting a transmission-position-dependent feature value that will be obtained from a reception signal. The error group calculation unit calculates an error group of measured feature values from reference transmission sources having known positions and predicted feature values calculated using the prediction model. The error combination unit combines the error group according to the positional relationship between a pseudo transmission source at a given position and the reference transmission sources and outputs a combined error. The pseudo measurement value correction unit uses the combined error to correct a predicted feature value predicted by the prediction model for signal reception from the pseudo transmission source. The position estimation unit inputs a measured transmission-position-dependent feature value into a learning model learned on the basis of predicted pseudo-transmission-source feature values that have been corrected and estimates a transmission source position.
Description
本発明は、位置推定装置、位置推定方法及びプログラムに関する。
The present invention relates to a position estimation device, a position estimation method and a program.
既存の発信源位置推定システムの一例が、特許文献1に記載されている。特許文献1に開示されたように、既存の発信源位置推定システムは、受信手段と、信号処理手段と、記憶手段と、位置推定手段と、を含む。
An example of an existing source position estimation system is described in Patent Document 1. As disclosed in Patent Document 1, the existing source position estimation system includes a receiving means, a signal processing means, a storage means, and a position estimating means.
受信手段は、発信源からの電波を受信する。受信手段は、互いに異なった位置に配置されている。信号処理手段は、複数の候補位置のそれぞれから発信された信号を取得して信号処理を行う。記憶手段は、信号処理手段による信号処理の結果として得られる特徴量のセットを保持する。位置推定手段は、推定対象の位置から発信された信号を受信手段で計測して得られた特徴量のセットと、記憶手段が保持する特徴量のセットと、を比較して発信源の位置を推定する。
The receiving means receives radio waves from the source. The receiving means are arranged at different positions from each other. The signal processing means acquires signals transmitted from each of the plurality of candidate positions and performs signal processing. The storage means holds a set of features obtained as a result of signal processing by the signal processing means. The position estimation means compares the set of features obtained by measuring the signal transmitted from the position to be estimated by the receiving means with the set of features held by the storage means to determine the position of the source. presume.
上記構成を有する既存の発信源位置推定システムは、つぎのような2つのステップで動作する。
The existing source position estimation system having the above configuration operates in the following two steps.
第1のステップは「訓練」のステップである。
The first step is the "training" step.
第1のステップである訓練段階では、発信源位置の候補となる既知の位置から電波が発信され、互いに異なった既知の位置に配置された受信手段が受信する。信号処理手段は、受信信号に対して信号処理を施し特徴量を取得する。受信手段の数と等しい数の特徴量のセットは、その発信源位置に対する位置指紋とも呼ばれる。訓練用データは、発信源位置の候補となる複数の位置に対して取得され、記憶手段に保持される。
In the training stage, which is the first step, radio waves are transmitted from known positions that are candidates for source positions, and are received by receiving means arranged at different known positions. The signal processing means performs signal processing on the received signal to acquire the feature amount. A set of features equal to the number of receiving means is also called a position fingerprint with respect to its source position. The training data is acquired for a plurality of positions that are candidates for the source position and is stored in the storage means.
第2のステップは「推定」のステップである。
The second step is the "estimation" step.
第2のステップである推定段階では、位置が未知の発信源に対して、訓練段階と同様の手順で位置指紋が取得される。そして、あらかじめ訓練時に作成した位置指紋のうち、推定対象の位置指紋と最も近い位置指紋に対応する位置が、発信源の推定位置として扱われる。
In the estimation stage, which is the second step, a position fingerprint is acquired for a source whose position is unknown by the same procedure as in the training stage. Then, among the position fingerprints created in advance during training, the position corresponding to the position fingerprint closest to the position fingerprint to be estimated is treated as the estimated position of the source.
特許文献1には、特徴量として、受信強度(RSS;Received Signal Strength)やチャネルインパルス応答(CIR;Channel Impulse Response)を用いることが開示されている。また、当該文献には、CIRのデータセットに対して、発信された信号の中心周波数や帯域幅や位置の補間することで、訓練時に用いる発信信号とは異なる信号が発信された場合にも位置推定ができるように位置指紋のデータを拡張する手法が開示されている。位置に対して補間を行うためのアルゴリズムとしては、線形、立体、スプライン補間のほか、クリギングを用いる手法がある。
Patent Document 1 discloses that reception strength (RSS; Received Signal Strength) and channel impulse response (CIR; Channel Impulse Response) are used as feature quantities. In addition, the document also states that by interpolating the center frequency, bandwidth, and position of the transmitted signal with respect to the CIR data set, a signal different from the transmitted signal used during training is transmitted. A method for expanding the position fingerprint data so that it can be estimated is disclosed. As an algorithm for performing interpolation for a position, there are a method using kriging in addition to linear, solid, and spline interpolation.
特許文献1に開示されたような既存の発信源位置推定システムには、位置の補間の際に、訓練段階で十分な数の候補位置に対して位置指紋を取得しておくことが必要という問題がある。
An existing source position estimation system as disclosed in Patent Document 1 has a problem that it is necessary to acquire position fingerprints for a sufficient number of candidate positions at the training stage when interpolating positions. There is.
その理由は、候補位置の数が十分でない場合、単純に候補位置毎の位置指紋を重ね合わせただけでは、その間に位置する発信源に対応する位置指紋を合成できない可能性があるためである。例えば、各々の候補位置から同時に電波が発信された場合に取得されるような位置指紋が合成されることもあり得る。
The reason is that if the number of candidate positions is not sufficient, it may not be possible to synthesize the position fingerprints corresponding to the sources located in between by simply superimposing the position fingerprints for each candidate position. For example, a position fingerprint that is acquired when radio waves are simultaneously transmitted from each candidate position may be synthesized.
しかし、数多くの位置指紋を取得することはコスト増加の原因となる。
However, acquiring a large number of position fingerprints causes an increase in cost.
本発明は、訓練段階で取得する位置指紋の数を減らすことに寄与する、位置推定装置、位置推定方法及びプログラムを提供することを主たる目的とする。
A main object of the present invention is to provide a position estimation device, a position estimation method, and a program that contribute to reducing the number of position fingerprints acquired in the training stage.
本発明の第1の視点によれば、任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する、予測モデル生成部と、既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する、誤差群算出部と、任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する、誤差合成部と、前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する、疑似計測値補正部と、前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する位置推定部と、を備える、位置推定装置が提供される。
According to the first viewpoint of the present invention, a prediction model generation that predicts a feature amount depending on a transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position. A unit, a measured value of a feature amount acquired based on a signal from a reference source at a known position, and a predicted value of a feature amount related to the reference source at a known position calculated using the prediction model. The error group is calculated according to the positional relationship between the error group calculation unit, the pseudo source at an arbitrary position, and each of the reference sources, which calculates the error group by calculating the error for a plurality of reference source positions. Pseudo-measured value correction that uses the synthesis error to correct the predicted value of the feature amount by the prediction model when the signal from the pseudo source and the error synthesis unit, which synthesizes and outputs as a synthesis error, is received. The source position is estimated by inputting the measured value of the feature amount depending on the unit and the transmission position into the learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. A position estimation device including a position estimation unit is provided.
本発明の第2の視点によれば、位置推定装置において、任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成するステップと、既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出するステップと、任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力するステップと、前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正するステップと、前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定するステップと、を含む、位置推定方法が提供される。
According to the second viewpoint of the present invention, in the position estimation device, a prediction model for predicting a feature amount depending on the transmission position obtained when a signal from a source at an arbitrary position is received at a specific position is generated. Steps to be performed, the measured value of the feature amount acquired based on the signal from the reference source at the known position, and the predicted value of the feature amount related to the reference source at the known position calculated using the prediction model. The error group is calculated by calculating the error of the above for a plurality of reference source positions, and the error group is synthesized according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources. A step of outputting as a synthesis error, a step of correcting the predicted value of the feature amount by the prediction model when a signal from the pseudo transmission source is received by using the synthesis error, and a feature amount depending on the transmission position. Provided is a position estimation method including a step of estimating the source position by inputting the measured value of the above into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. Will be done.
本発明の第3の視点によれば、位置推定装置に搭載されたコンピュータに、任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する処理と、既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する処理と、任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する処理と、前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する処理と、前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する処理と、を実行させるプログラムが提供される。
According to the third viewpoint of the present invention, the computer mounted on the position estimation device predicts the feature amount depending on the transmission position obtained when a signal from a source at an arbitrary position is received at a specific position. The process of generating the prediction model to be performed, the measured value of the feature amount acquired based on the signal from the reference source at the known position, and the feature amount related to the reference source at the known position calculated using the prediction model. The error group is calculated by calculating the error between the predicted value and the reference source positions for a plurality of reference source positions, and the error is calculated according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources. A process of synthesizing a group and outputting it as a synthesis error, a process of correcting a predicted value of a feature amount by the prediction model when a signal from the pseudo source is received by using the synthesis error, and a process of correcting the transmission position. By inputting the measured value of the feature amount depending on the above into the learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source, the process of estimating the source position is executed. The program is provided.
本発明の各視点によれば、訓練段階で取得する位置指紋の数を減らすことに寄与する、位置推定装置、位置推定方法及びプログラムが提供される。本発明により、当該効果の代わりに、又は当該効果と共に、他の効果が奏されてもよい。
According to each viewpoint of the present invention, a position estimation device, a position estimation method, and a program that contribute to reducing the number of position fingerprints acquired in the training stage are provided. According to the present invention, other effects may be produced in place of or in combination with the effect.
はじめに、一実施形態の概要について説明する。なお、この概要に付記した図面参照符号は、理解を助けるための一例として各要素に便宜上付記したものであり、この概要の記載はなんらの限定を意図するものではない。なお、本明細書及び図面において、同様に説明されることが可能な要素については、同一の符号を付することにより重複説明が省略され得る。
First, the outline of one embodiment will be explained. It should be noted that the drawing reference reference numerals added to this outline are added to each element for convenience as an example to aid understanding, and the description of this outline is not intended to limit anything. In the present specification and the drawings, elements that can be similarly described may be given the same reference numerals, so that duplicate description may be omitted.
一実施形態に係る位置推定装置100は、予測モデル生成部101と、誤差群算出部102と、誤差合成部103と、疑似計測値補正部104と、位置推定部105と、を備える(図1参照)。予測モデル生成部101は、任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する。誤差群算出部102は、既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、予測モデルを用いて算出された、既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する。誤差合成部103は、任意位置の疑似発信源と参照発信源各々との位置関係に応じて誤差群を合成し、合成誤差として出力する。疑似計測値補正部104は、疑似発信源からの信号を受信したときの予測モデルによる特徴量の予測値を、合成誤差を用いて補正する。位置推定部105は、発信位置に依存した特徴量の計測値を、少なくとも補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する。
The position estimation device 100 according to one embodiment includes a prediction model generation unit 101, an error group calculation unit 102, an error synthesis unit 103, a pseudo measurement value correction unit 104, and a position estimation unit 105 (FIG. 1). reference). The prediction model generation unit 101 generates a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position. The error group calculation unit 102 includes a measured value of the feature amount acquired based on the signal from the reference source at the known position, and a predicted value of the feature amount related to the reference source at the known position calculated using the prediction model. The error group is calculated by calculating the error of, for a plurality of reference source positions. The error synthesis unit 103 synthesizes an error group according to the positional relationship between the pseudo transmission source at an arbitrary position and each of the reference transmission sources, and outputs it as a synthesis error. The pseudo-measured value correction unit 104 corrects the predicted value of the feature amount by the prediction model when the signal from the pseudo-source is received by using the synthesis error. The position estimation unit 105 estimates the source position by inputting the measured value of the feature amount depending on the transmission position into the learning model learned at least based on the predicted value of the feature amount from the corrected pseudo source. To do.
上記位置推定装置100は、例えば、発信される電波を周囲に配置されたセンサで受信し、当該受信波形から得られる発信位置に依存した特徴量を用いて発信源位置を推定する。上記位置推定装置100は、単純に複数候補位置に対応する位置指紋を重ね合わせるのではなく、複数候補位置での計測値と予測値の誤差の空間分布を重ね合わせ、当該予測値の誤差の空間分布を用いて予測値を補正する。即ち、上記位置推定装置100は、発信源から遠ざかるほど特徴量(例えば、RSS)が小さくなるといった通常の傾向を考慮することで、少ない数の候補位置での位置指紋から、任意の発信位置に対応する位置指紋を作成する。作成された位置指紋により、実測値による訓練データが拡張される。その結果、訓練段階において、実際に計測して取得する位置指紋の数を削減できる。
The position estimation device 100 receives, for example, a transmitted radio wave by sensors arranged around it, and estimates the source position using a feature amount depending on the transmission position obtained from the received waveform. The position estimation device 100 does not simply superimpose the position fingerprints corresponding to the plurality of candidate positions, but superimposes the spatial distribution of the error between the measured value and the predicted value at the plurality of candidate positions, and the space of the error of the predicted value. Correct the predicted value using the distribution. That is, the position estimation device 100 takes into consideration the usual tendency that the feature amount (for example, RSS) becomes smaller as the distance from the source is increased, so that the position fingerprints at a small number of candidate positions can be moved to an arbitrary transmission position. Create a corresponding position fingerprint. The created position fingerprint expands the training data based on the measured values. As a result, the number of position fingerprints actually measured and acquired at the training stage can be reduced.
さらに、発信源から遠ざかるほどRSSが小さくなるといった通常の傾向を考慮することで、訓練段階で位置指紋を取得した候補位置の外側の発信位置に対応する位置指紋が精度よく作成される。その結果、訓練段階で位置指紋を取得した候補位置の外側に発信源が存在する場合であっても、推定精度の劣化度合いを低減できる。
Furthermore, by considering the usual tendency that RSS becomes smaller as the distance from the source increases, the position fingerprint corresponding to the transmission position outside the candidate position for which the position fingerprint was acquired in the training stage is accurately created. As a result, even when the source exists outside the candidate position where the position fingerprint was acquired in the training stage, the degree of deterioration of the estimation accuracy can be reduced.
以下に具体的な実施形態について、図面を参照してさらに詳しく説明する。
The specific embodiment will be described in more detail below with reference to the drawings.
[第1の実施形態]
第1の実施形態について、図面を用いてより詳細に説明する。 [First Embodiment]
The first embodiment will be described in more detail with reference to the drawings.
第1の実施形態について、図面を用いてより詳細に説明する。 [First Embodiment]
The first embodiment will be described in more detail with reference to the drawings.
[構成の説明]
はじめに、第1の実施形態に係る発信源位置推定システムの構成について説明する。図2は、第1の実施形態に係る発信源位置推定システムの概略構成の一例を示す図である。 [Description of configuration]
First, the configuration of the source position estimation system according to the first embodiment will be described. FIG. 2 is a diagram showing an example of a schematic configuration of a source position estimation system according to the first embodiment.
はじめに、第1の実施形態に係る発信源位置推定システムの構成について説明する。図2は、第1の実施形態に係る発信源位置推定システムの概略構成の一例を示す図である。 [Description of configuration]
First, the configuration of the source position estimation system according to the first embodiment will be described. FIG. 2 is a diagram showing an example of a schematic configuration of a source position estimation system according to the first embodiment.
図2を参照すると、発信源位置推定システムには、複数の受信装置10-1~10-Mと、複数の信号処理装置20-1~20-Mと、記憶装置30と、位置推定装置40と、が含まれる(Mは正の整数、以下同じ)。なお、以降の説明において、受信装置10-1~10-Mを区別する特段の理由がない場合には、単に「受信装置10」と表記する。同様に、信号処理装置20-1~20-Mを区別する特段の理由がない場合には、単に「信号処理装置20」と表記する。
Referring to FIG. 2, the source position estimation system includes a plurality of receiving devices 10-1 to 10-M, a plurality of signal processing devices 20-1 to 20-M, a storage device 30, and a position estimating device 40. And are included (M is a positive integer, the same applies hereinafter). In the following description, if there is no particular reason for distinguishing the receiving devices 10-1 to 10-M, it is simply referred to as "receiving device 10". Similarly, unless there is a particular reason for distinguishing the signal processing devices 20-1 to 20-M, the term "signal processing device 20" is simply used.
受信装置10は、発信源位置推定の対象となるフィールドに配置される。受信装置10には、電波を受信する電波センサ等が例示される。
The receiving device 10 is arranged in the field to be the target of the source position estimation. The receiving device 10 is exemplified by a radio wave sensor or the like that receives radio waves.
信号処理装置20は、受信装置10が受信した信号を使用して信号処理を行う装置である。具体的には、信号処理装置20は、受信信号(無線信号)から特徴量(例えば、受信強度RSS)を計算する。
The signal processing device 20 is a device that performs signal processing using the signal received by the receiving device 10. Specifically, the signal processing device 20 calculates a feature amount (for example, reception intensity RSS) from a received signal (radio signal).
記憶装置30は、受信装置10の位置と信号処理装置20により計算された特徴量を関連付けて記憶する。
The storage device 30 stores the position of the receiving device 10 in association with the feature amount calculated by the signal processing device 20.
位置推定装置40は、記憶装置30に格納された情報に基づき、位置が未知の信号発信源のフィールド内における位置を推定する。
The position estimation device 40 estimates the position in the field of a signal source whose position is unknown based on the information stored in the storage device 30.
位置推定装置40は、図3に示すようなフィールドにおいて、発信源の位置を推定する。図3において、電波センサが上記受信装置10に相当する。位置推定装置40は、参照発信源の位置ごとの特徴量マップ(例えば、電波強度のマップ;位置指紋)を生成し、当該マップと未知発信源から得られる特徴量マップ(位置指紋)を照合し、未知発信源の位置を推定する。
The position estimation device 40 estimates the position of the source in the field as shown in FIG. In FIG. 3, the radio wave sensor corresponds to the receiving device 10. The position estimation device 40 generates a feature amount map (for example, a radio wave intensity map; position fingerprint) for each position of the reference source, and collates the map with the feature amount map (position fingerprint) obtained from an unknown source. , Estimate the location of an unknown source.
図4は、第1の実施形態に係る位置推定装置40の処理構成(処理モジュール)の一例を示す図である。
FIG. 4 is a diagram showing an example of a processing configuration (processing module) of the position estimation device 40 according to the first embodiment.
図4を参照すると、位置推定装置40は、入力部201と、データ分類部202と、訓練データ拡張部203と、訓練データ結合部204と、学習部205と、位置推定部206と、出力部207と、を含んで構成される。
Referring to FIG. 4, the position estimation device 40 includes an input unit 201, a data classification unit 202, a training data expansion unit 203, a training data combination unit 204, a learning unit 205, a position estimation unit 206, and an output unit. 207 and.
入力部201は、任意位置の発信源からの信号を特定の位置で受信したときのRSS計測値セットを入力する。
The input unit 201 inputs an RSS measurement value set when a signal from a source at an arbitrary position is received at a specific position.
データ分類部202は、入力されたデータが訓練用のデータか推定用のデータを分類する。
The data classification unit 202 classifies the input data as training data or estimation data.
訓練データ拡張部203は、訓練用データを基に疑似的にデータ(訓練用のデータ)を生成する。
The training data expansion unit 203 generates pseudo data (training data) based on the training data.
訓練データ結合部204は、実際に計測して得られた訓練用データと疑似的に生成された訓練用データを結合する。訓練データ結合部204は、上記2つの訓練用データ(実測値による訓練用データ、擬似的に生成された訓練用データ)を学習用の訓練用データとして生成する。
The training data combination unit 204 combines the training data obtained by actually measuring and the training data generated in a pseudo manner. The training data combining unit 204 generates the above two training data (training data based on actual measurement values, training data generated in a pseudo manner) as training data for training.
学習部205は、結合された訓練用データに基づきRSS計測値セットと正解位置の対応関係を学習する。より具体的には、学習部205は、参照発信源からの信号に基づき取得された特徴量の計測値(実測値による訓練用データ)と疑似発信源からの特徴量の予測値(疑似発信源を想定した場合の訓練用データ)に基づき、上記対応関係を学習する。
The learning unit 205 learns the correspondence between the RSS measurement value set and the correct position based on the combined training data. More specifically, the learning unit 205 has a measured value of the feature amount acquired based on the signal from the reference source (training data based on the measured value) and a predicted value of the feature amount from the pseudo source (pseudo source). Based on the training data), the above correspondence is learned.
位置推定部206は、学習部205が学習した結果に基づき、入力された推定用データに対応する発信源位置を推定する。位置推定部206は、発信位置に依存した特徴量の計測値を、少なくとも補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデル(学習部205により生成された学習モデル)に入力することで、発信源位置を推定する。
The position estimation unit 206 estimates the source position corresponding to the input estimation data based on the result learned by the learning unit 205. The position estimation unit 206 is a learning model (learning model generated by the learning unit 205) in which the measured value of the feature amount depending on the transmission position is learned based on at least the predicted value of the feature amount from the corrected pseudo source. The source position is estimated by inputting to.
出力部207は、推定された発信源位置を出力する。
The output unit 207 outputs the estimated source position.
ここで、RSSは、発信位置に依存した特徴量の一例である。各々異なるM個の位置で計測されたRSS計測値のセット[RSS1、RSS2、・・・、RSSM]が、位置指紋の一例である。例えば、図3の例では、9個のRSSからなるRSS計測値セットが計算される。
Here, RSS is an example of a feature amount depending on the transmission position. A set of RSS measurement values [RSS 1 , RSS 2 , ..., RSS M ] measured at M different positions is an example of a position fingerprint. For example, in the example of FIG. 3, an RSS measurement value set consisting of 9 RSSs is calculated.
訓練用データは、RSS位置指紋Xi(=[RSS1、RSS2、・・・、RSSM])と、それに対応する訓練用の参照発信源の位置Yiの対の集合{(Xi、Yi)}である。
The training data is a set of pairs of RSS position fingerprint Xi (= [RSS 1 , RSS 2 , ..., RSS M ]) and the corresponding training reference source position Yi {(Xi, Yi). }.
なお、訓練段階での参照発信源の数をN(Nは正の整数。以下同じ)とする。サフィックスiは1~Nである。図3の例では、1個の参照発信源(N=1)が記載されている。
Note that the number of reference sources at the training stage is N (N is a positive integer. The same applies hereinafter). The suffix i is 1 to N. In the example of FIG. 3, one reference source (N = 1) is described.
上述のように、RSSは本願開示で使用できる特徴量の一例であり、他の特徴量として、CIRや基準時刻と到来時刻の差等が用いられてもよい。
As described above, RSS is an example of the feature amount that can be used in the disclosure of the present application, and as another feature amount, CIR or the difference between the reference time and the arrival time may be used.
入力部201は、RSS計測値の線形単位と対数単位の相互変換、正規化や標準化といったスケーリングなどの単調写像を伴う前処理を含んでもよい。
The input unit 201 may include preprocessing accompanied by monotonic mapping such as mutual conversion between linear units and logarithmic units of RSS measurement values, scaling such as normalization and standardization.
データ分類部202は、発信源位置推定システムのモードに応じて入力部201から取得したデータを「訓練用データ」又は「推定用データ」に振り分ける。発信源位置推定システムのモードは、「訓練モード」及び「推定モード」のいずれかであり、システムの管理者等が発信源位置推定システムのモードを決定し、入力する。
The data classification unit 202 distributes the data acquired from the input unit 201 into "training data" or "estimation data" according to the mode of the source position estimation system. The mode of the source position estimation system is either "training mode" or "estimation mode", and the system administrator or the like determines and inputs the mode of the source position estimation system.
訓練データ拡張部203は、伝搬モデル生成部211と、参照発信源誤差セット作成部212と、疑似発信源位置決定部213と、モデルベース疑似計測値セット生成部214と、疑似発信源用誤差セット合成部215と、疑似計測値セット補正部216と、を含む。
The training data expansion unit 203 includes a propagation model generation unit 211, a reference source error set creation unit 212, a pseudo source position determination unit 213, a model-based pseudo measurement value set generation unit 214, and a pseudo source error set. It includes a synthesis unit 215 and a pseudo measurement value set correction unit 216.
伝搬モデル生成部211は、訓練用データを用いて電波伝搬モデルを生成する。伝搬モデル生成部211は、任意位置の発信源(参照発信源)からの信号を特定の位置で受信装置10が受信したときに得られる、発信位置に依存した特徴量(例えば、電波強度)を予測するモデルを生成する。当該予測モデルの詳細は後述する。
The propagation model generation unit 211 generates a radio wave propagation model using the training data. The propagation model generation unit 211 obtains a feature amount (for example, radio field intensity) depending on the transmission position, which is obtained when the receiving device 10 receives a signal from a transmission source (reference transmission source) at an arbitrary position at a specific position. Generate a model to predict. The details of the prediction model will be described later.
参照発信源誤差セット作成部212は、生成された伝搬モデルから予測されるRSS疑似計測値セットと実際に得られたRSS計測値セットとの誤差のセットを作成する。参照発信源誤差セット作成部212は、既知位置の参照発信源からの信号に基づき取得された特徴量の計測値(実測値)と、上記予測モデルを用いて算出された、既知位置の参照発信源に関する特徴量の予測値と、誤差を計算する。参照発信源誤差セット作成部212は、上記誤差の計算を複数の参照発信源位置に対して実行することで誤差群(複数の誤差マップ)を算出する。
The reference source error set creation unit 212 creates a set of errors between the RSS pseudo-measurement value set predicted from the generated propagation model and the RSS measurement value set actually obtained. The reference source error set creation unit 212 uses the measured value (actual measurement value) of the feature amount acquired based on the signal from the reference source at the known position and the reference transmission at the known position calculated using the above prediction model. Calculate the predicted value of the feature quantity for the source and the error. The reference source error set creation unit 212 calculates an error group (plurality of error maps) by executing the above error calculation for a plurality of reference source positions.
疑似発信源位置決定部213は、訓練データ拡張のために設ける疑似的な発信源の位置を決定する。
The pseudo source position determination unit 213 determines the position of the pseudo source provided for training data expansion.
モデルベース疑似計測値セット生成部214は、疑似発信源位置と伝搬モデルに基づいた疑似的なRSS計測値セットを生成する。
The model-based pseudo measurement value set generation unit 214 generates a pseudo RSS measurement value set based on the pseudo source position and the propagation model.
疑似発信源用誤差セット合成部215は、任意位置の疑似発信源と参照発信源各々との位置関係に応じて誤差群(複数の誤差マップ)を合成し、合成誤差として出力する。例えば、疑似発信源用誤差セット合成部215は、疑似発信源位置と参照発信源位置の空間的な相関の度合いに基づき誤差セットを合成して疑似発信源用誤差セットを合成する。
The error set synthesis unit 215 for the pseudo source synthesizes an error group (plurality of error maps) according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources, and outputs it as a composite error. For example, the error set synthesis unit 215 for a pseudo source synthesizes an error set based on the degree of spatial correlation between the pseudo source position and the reference source position, and synthesizes the error set for the pseudo source.
疑似計測値セット補正部216は、生成されたモデルベース疑似計測値セットを合成された疑似発信源用誤差セットで補正して出力する。疑似計測値セット補正部216は、疑似発信源からの信号を受信したときの予測モデルによる特徴量の予測値を、上記合成誤差を用いて補正する。
The pseudo measurement value set correction unit 216 corrects the generated model-based pseudo measurement value set with the combined error set for the pseudo source and outputs it. The pseudo measurement value set correction unit 216 corrects the predicted value of the feature amount by the prediction model when the signal from the pseudo transmission source is received by using the above synthesis error.
ここで、伝搬モデル生成部211は、予測モデル生成部の一例である。また、参照発信源誤差セット作成部212は、誤差群算出部の一例である。また、疑似発信源用誤差セット合成部215は、誤差合成部の一例である。
Here, the propagation model generation unit 211 is an example of the prediction model generation unit. Further, the reference source error set creation unit 212 is an example of an error group calculation unit. Further, the error set synthesis unit 215 for a pseudo transmission source is an example of an error synthesis unit.
[動作の説明]
次に、フローチャートを参照して第1の実施形態に係る発信源位置推定システムの動作について詳細に説明する。図5は、第1の実施形態に係る発信源位置推定システムの訓練段階の動作の一例を示すフローチャートである。はじめに、図5のフローチャートを参照して訓練段階の動作について詳細に説明する。 [Explanation of operation]
Next, the operation of the source position estimation system according to the first embodiment will be described in detail with reference to the flowchart. FIG. 5 is a flowchart showing an example of the operation of the training stage of the source position estimation system according to the first embodiment. First, the operation of the training stage will be described in detail with reference to the flowchart of FIG.
次に、フローチャートを参照して第1の実施形態に係る発信源位置推定システムの動作について詳細に説明する。図5は、第1の実施形態に係る発信源位置推定システムの訓練段階の動作の一例を示すフローチャートである。はじめに、図5のフローチャートを参照して訓練段階の動作について詳細に説明する。 [Explanation of operation]
Next, the operation of the source position estimation system according to the first embodiment will be described in detail with reference to the flowchart. FIG. 5 is a flowchart showing an example of the operation of the training stage of the source position estimation system according to the first embodiment. First, the operation of the training stage will be described in detail with reference to the flowchart of FIG.
ステップS31は、訓練段階の準備工程である。本ステップにおいて、発信源位置推定システムは、発信源(参照発信源)からの信号を受信して生成される位置座標セットを生成する。受信位置座標は、レーザー測距計などを用いて基準点からの距離としてシステムに入力されてもよいし、衛星測位システムなどを使うことによって入力されてもよい。なお、受信位置は、領域内の任意の位置に発信源があった場合に複数箇所で電波を受信できるように設定することが望ましい。例えば、図3の例では、9個の電波センサが配置されている位置がシステムに入力される。
Step S31 is a preparatory step for the training stage. In this step, the source position estimation system receives a signal from the source (reference source) and generates a set of position coordinates generated. The reception position coordinates may be input to the system as a distance from the reference point using a laser range finder or the like, or may be input by using a satellite positioning system or the like. It is desirable to set the reception position so that radio waves can be received at a plurality of locations when the transmission source is located at an arbitrary position in the area. For example, in the example of FIG. 3, the position where the nine radio wave sensors are arranged is input to the system.
次に、発信源位置推定システムは、位置が既知である参照発信源を用いて訓練用データを作成する(ステップS32)。
Next, the source position estimation system creates training data using a reference source whose position is known (step S32).
訓練用データは、複数の参照発信源位置と、それに対応して得られるRSS計測値セットの集合である。例えば、図3の例では、参照発信源の位置を変更し、各位置における電波センサ(受信装置10)から受信信号が収集される。当該参照発信源の位置ごとの受信信号から特徴量(例えば、RSS)が計算され、RSS計測値セット(位置指紋)が得られる。
The training data is a set of multiple reference source positions and RSS measurement value sets obtained correspondingly. For example, in the example of FIG. 3, the position of the reference transmission source is changed, and the received signal is collected from the radio wave sensor (reception device 10) at each position. A feature amount (for example, RSS) is calculated from the received signal for each position of the reference source, and an RSS measurement value set (position fingerprint) is obtained.
図6は、RSS計測値セットの概念を説明するための図である。図6では、図3に示すようにフィールド内に9個の電波センサを設置した場合の当該センサから得られる電波強度を色の濃淡で表現している。図6を参照すると、参照発信源から送信された電波を電波センサ(受信装置10)で受信し、各電波センサの位置と当該センサから得られる特徴量を関連付けた情報がRSS計測値セット(位置指紋)となる。この位置指紋と参照発信源の位置を対応付けた情報が訓練用データである。
FIG. 6 is a diagram for explaining the concept of the RSS measurement value set. In FIG. 6, when nine radio wave sensors are installed in the field as shown in FIG. 3, the radio wave intensity obtained from the sensors is expressed by shades of color. Referring to FIG. 6, the radio wave sensor (receiver 10) receives the radio wave transmitted from the reference source, and the information relating the position of each radio wave sensor and the feature amount obtained from the sensor is the RSS measurement value set (position). Fingerprint). The information in which the position fingerprint is associated with the position of the reference source is the training data.
図5に説明を戻す。発信源位置推定システムは、実際に計測して得られた訓練用データをもとにして、疑似発信源による訓練用データを作成する(ステップS33)。疑似発信源による訓練用データの作成に関する詳細は後述する。
Return the explanation to Fig. 5. The source position estimation system creates training data by a pseudo source based on the training data obtained by actually measuring (step S33). Details regarding the creation of training data by the pseudo source will be described later.
さらに、発信源位置推定システムは、上記ステップS32で作成された訓練用データと、ステップS33で作成された疑似発信源による訓練用データを結合して得られる訓練データを用いて、位置推定器を訓練する(ステップS34)。即ち、発信源位置推定システムは、2つの訓練データ(実際の参照発信源による訓練用データと疑似発信源による訓練用データ)を用いて学習モデルを生成する。
Further, the source position estimation system uses the training data created by combining the training data created in step S32 and the training data created by the pseudo source created in step S33 to obtain a position estimator. Train (step S34). That is, the source position estimation system generates a learning model using two training data (training data by an actual reference source and training data by a pseudo source).
図7は、上記ステップS32における参照発信源による訓練用データを作成する動作の一例を示すフローチャートである。図7を参照し、上記ステップS32の動作を詳細に説明する。
FIG. 7 is a flowchart showing an example of an operation of creating training data by the reference source in step S32. The operation of step S32 will be described in detail with reference to FIG. 7.
はじめに、受信装置10により参照発信源が発信する電波が複数の位置で受信され、信号処理装置20によりRSSが計測される(ステップS41)。
First, the radio waves transmitted by the reference source are received by the receiving device 10 at a plurality of positions, and the RSS is measured by the signal processing device 20 (step S41).
次に、発信源位置推定システムは、参照発信源の位置を取得する(ステップS42)。
Next, the source position estimation system acquires the position of the reference source (step S42).
ステップS41で得られる複数のRSS計測値のセットが位置指紋である。当該位置指紋とステップS42で取得する参照発信源位置は1:1の対応関係にある。すなわち、ステップS41とステップS42が実行されることにより、訓練用データが得られる。
A set of a plurality of RSS measurement values obtained in step S41 is a position fingerprint. There is a 1: 1 correspondence between the position fingerprint and the reference source position acquired in step S42. That is, training data is obtained by executing step S41 and step S42.
なお、参照発信源の数は、受信位置の数と同程度から5倍程度の数が望ましい。それよりも少ないと、参照発信源による訓練用データから疑似発信源による訓練用データを生成する際の精度が劣化し、高い位置精度が得られなくなる。また、それよりも多いと、位置推定精度向上の観点からは望ましいが、訓練段階で必要なデータ量を削減するという効果が小さくなる。
It is desirable that the number of reference transmission sources is about the same as the number of reception positions to about 5 times as many. If it is less than that, the accuracy when generating the training data by the pseudo source from the training data by the reference source deteriorates, and high position accuracy cannot be obtained. Further, if it is more than that, it is desirable from the viewpoint of improving the position estimation accuracy, but the effect of reducing the amount of data required at the training stage is reduced.
次に、得られた訓練用データの各々の参照発信源位置に対して、以下の処理が行われる。
Next, the following processing is performed for each reference source position of the obtained training data.
訓練データ拡張部203の伝搬モデル生成部211は、参照発信源位置と実際のRSS計測値セットに基づき、伝搬モデルを作成(推定)する(ステップS43)。具体的には、伝搬モデル生成部211は、発信源と受信位置の間の距離に応じて、どの程度RSSが減衰するかをモデル化する。
The propagation model generation unit 211 of the training data expansion unit 203 creates (estimates) a propagation model based on the reference source position and the actual RSS measurement value set (step S43). Specifically, the propagation model generation unit 211 models how much RSS is attenuated according to the distance between the source and the reception position.
図8は、伝搬モデル生成部211の動作を説明するための図である。伝搬モデル生成部211は、参照発信源の位置を中心(原点)に設定し、位置が把握されている参照発信源の位置と電波センサ(受信装置10)の間の距離をX軸、各電波センサにおける電波強度をY軸に設定し、データセット(グラフ)を生成する。伝搬モデル生成部211は、当該データセットに対して回帰解析を実施して伝搬モデルを生成する。図8の例では、直線が伝搬モデルとして生成される。伝搬モデル生成部211は、図8の直線を表す定数(伝搬モデル定数)を計算する。
FIG. 8 is a diagram for explaining the operation of the propagation model generation unit 211. The propagation model generation unit 211 sets the position of the reference source as the center (origin), sets the distance between the position of the reference source whose position is known and the radio wave sensor (receiver 10) on the X-axis, and each radio wave. The radio field intensity in the sensor is set on the Y-axis, and a data set (graph) is generated. The propagation model generation unit 211 performs regression analysis on the data set to generate a propagation model. In the example of FIG. 8, a straight line is generated as a propagation model. The propagation model generation unit 211 calculates a constant (propagation model constant) representing the straight line shown in FIG.
図7に説明を戻す。参照発信源誤差セット作成部212は、上記作成された伝搬モデルに基づき、各受信位置におけるRSSの予測値を計算する(ステップS44)。
Return the explanation to Fig. 7. The reference source error set creation unit 212 calculates the predicted RSS value at each reception position based on the propagation model created above (step S44).
図9は、参照発信源誤差セット作成部212の動作を説明するための図である。参照発信源誤差セット作成部212は、図9の左下に示すような電波強度マップを生成する。参照発信源誤差セット作成部212は、上記ステップS43にて推定された伝搬モデルに参照発信源と電波センサ(受信装置10)間の距離を入力し、電波センサ位置における電波強度を計算する。参照発信源誤差セット作成部212は、フィールドに配置された各電波センサについて当該電波強度の計算を行い、図9の左下に示すような電波強度マップ(RSS予測値)を計算する。
FIG. 9 is a diagram for explaining the operation of the reference source error set creation unit 212. The reference source error set creation unit 212 generates a radio field intensity map as shown in the lower left of FIG. The reference source error set creation unit 212 inputs the distance between the reference source and the radio wave sensor (receiver 10) into the propagation model estimated in step S43, and calculates the radio wave intensity at the radio wave sensor position. The reference source error set creation unit 212 calculates the radio wave intensity of each radio wave sensor arranged in the field, and calculates a radio wave intensity map (RSS predicted value) as shown in the lower left of FIG.
その後、参照発信源誤差セット作成部212は、実際のRSS計測値と上記ステップS44で計算された予測値と差を計算し、誤差セットとして保存する(ステップS45)。図9の例では、左上の電波強度マップ(実際のRSS計測値)と左下の電波強度マップ(RSS予測値)の差分が計算され、参照発信源位置ごとの電波強度の誤差マップが計算される。図9を参照すると、当該図面の左下に記載された伝搬モデルにより生成された電波強度マップは現実のフィールドに存在する障害物の影響を受けない予測値であるので、発信源から離れるに従い電波強度が低くなる。対して、当該図面の左上の電波強度マップは、実測値であるのでフィールドに存在する障害物等の影響を受けている。電波強度に関する実測値と予測値の差分を計算すると、各参照発信源位置における障害物等の影響が誤差として計算される。なお、図9等に示す電波強度の誤差に関する濃淡は、中央の色は誤差が小さいことを示し、端の色は誤差(正の誤差、負の誤差)が大きいことを示す。
After that, the reference source error set creation unit 212 calculates the difference between the actual RSS measurement value and the predicted value calculated in step S44, and saves it as an error set (step S45). In the example of FIG. 9, the difference between the upper left radio field intensity map (actual RSS measurement value) and the lower left radio wave intensity map (RSS predicted value) is calculated, and the error map of the radio wave intensity for each reference source position is calculated. .. Referring to FIG. 9, the radio field intensity map generated by the propagation model described in the lower left of the drawing is a predicted value that is not affected by obstacles existing in the actual field, and therefore the radio field strength increases as the distance from the source increases. Will be low. On the other hand, since the radio field intensity map on the upper left of the drawing is an actually measured value, it is affected by obstacles existing in the field. When the difference between the measured value and the predicted value regarding the radio field strength is calculated, the influence of obstacles or the like at each reference source position is calculated as an error. The shading related to the error of the radio field intensity shown in FIG. 9 and the like indicates that the center color has a small error, and the edge color has a large error (positive error, negative error).
図10は、上記ステップS33における疑似発信源による訓練データを作成する動作の一例を示すフローチャートである。図10を参照し、上記ステップS33の動作を詳細に説明する。
FIG. 10 is a flowchart showing an example of an operation of creating training data by the pseudo source in step S33. The operation of step S33 will be described in detail with reference to FIG.
まず、疑似発信源位置決定部213は、疑似発信源の位置を設定する(ステップS51)。疑似発信源位置決定部213は、疑似発信源の位置を、位置推定対象である未知発信源が存在しうる領域内に設定する。その際、疑似発信源位置決定部213は、既に参照発信源が存在する位置とは異なる位置を、疑似発信源位置に設定することが望ましい。
First, the pseudo source position determination unit 213 sets the position of the pseudo source (step S51). The pseudo source position determination unit 213 sets the position of the pseudo source within a region where an unknown source, which is a position estimation target, can exist. At that time, it is desirable that the pseudo source position determination unit 213 sets a position different from the position where the reference source already exists as the pseudo source position.
また、領域内を網羅できるよう、疑似発信源位置決定部213は、複数の位置に疑似発信源を設定することが望ましい。具体的には、疑似発信源の数は、参照発信源の数の5倍から10倍程度が望ましい。それよりも少ないと十分な密度で領域内を網羅できないことがあり、また、それよりも多くしても計算量が増すだけで精度の向上は限定的となる。
Further, it is desirable that the pseudo source position determination unit 213 sets the pseudo source at a plurality of positions so as to cover the entire area. Specifically, the number of pseudo-sources is preferably about 5 to 10 times the number of reference sources. If it is less than that, it may not be possible to cover the entire region with sufficient density, and if it is more than that, the amount of calculation will increase and the improvement in accuracy will be limited.
次に、各々の疑似発信源位置に対して、以下の処理が行われる。
Next, the following processing is performed for each pseudo source position.
モデルベース疑似計測値セット生成部214は、上記ステップS43で生成された伝搬モデルに基づき、疑似発信源位置から発信される電波を各受信位置で受信したときに得られるRSS計測値セットを疑似計測値セットとして算出する(ステップS52)。モデルベース疑似計測値セット生成部214は、選択した疑似発信源の位置に最も近い参照発信源の伝搬モデルに疑似発信源の位置と電波センサの位置の差を入力し、疑似発信源ごとのRSS計測値セット(電波強度マップ)を生成する。
The model-based pseudo-measurement value set generation unit 214 pseudo-measures the RSS measurement value set obtained when the radio wave transmitted from the pseudo-source position is received at each reception position based on the propagation model generated in step S43. Calculated as a value set (step S52). The model-based pseudo-measurement value set generator 214 inputs the difference between the position of the pseudo-source and the position of the radio wave sensor into the propagation model of the reference source closest to the position of the selected pseudo-source, and RSS for each pseudo-source. Generate a measurement value set (radio field intensity map).
あるいは、上記疑似発信源ごとのRSS計測値セットは、全ての疑似発信源に共通して使用される伝搬モデルから生成されてもよい。具体的には、伝搬モデル生成部211は、全ての参照発信源に対するデータを図8に示すようにプロットし、当該プロットされたデータから上記全ての疑似発信源に共通して使用される伝搬モデルを生成する。
Alternatively, the RSS measurement value set for each pseudo source may be generated from a propagation model that is commonly used for all pseudo sources. Specifically, the propagation model generation unit 211 plots the data for all the reference sources as shown in FIG. 8, and the propagation model commonly used for all the pseudo sources from the plotted data. To generate.
あるいは、上記疑似発信源ごとのRSS計測値セットは、疑似発信源位置と参照発信源の間の距離に応じて決定される重み係数で加重平均することで得られる伝搬モデルから生成されてもよい。この場合の伝搬モデルの生成方法は、後述する疑似発信源用誤差セット合成部215が誤差セットを生成する際の方法と同一とすることができる。
Alternatively, the RSS measurement value set for each pseudo source may be generated from a propagation model obtained by weighted averaging with a weighting coefficient determined according to the distance between the pseudo source position and the reference source. .. The method for generating the propagation model in this case can be the same as the method for generating the error set by the error set synthesis unit 215 for the pseudo source, which will be described later.
次に、疑似発信源用誤差セット合成部215は、ステップS45で保存された参照発信源位置毎の誤差セットを合成し、疑似発信源位置に応じた誤差セットを作成する(ステップS53)。例えば、疑似発信源用誤差セット合成部215は、参照源発信位置と疑似発信源位置との相対関係(2つの発信源の位置関係)に基づき予め決められた重み係数で誤差セットを加重平均することで、疑似発信源位置に対応する誤差セットを合成する。
Next, the error set synthesis unit 215 for the pseudo source synthesizes the error set for each reference source position saved in step S45, and creates an error set according to the pseudo source position (step S53). For example, the error set synthesis unit 215 for a pseudo source weights and averages the error set with a predetermined weighting coefficient based on the relative relationship between the reference source source position and the pseudo source position (positional relationship between the two sources). By doing so, the error set corresponding to the pseudo source position is synthesized.
図11は、疑似発信源用誤差セット合成部215の動作を説明するための図である。疑似発信源用誤差セット合成部215は、先に算出された各参照発信源位置の誤差セットを合成(電波強度を加算)する。その際、疑似発信源用誤差セット合成部215は、疑似発信源の位置と参照発信源の位置を反映した加重平均により誤差セットを合成する。例えば、図11の左側最上段においては、参照発信源と疑似発信源の距離は長いので当該参照発信源の重みは小さくする。一方、左側最下段においては、参照発信源と疑似発信源の距離は短いので当該参照発信源の重みは大きくする。このような誤差セットの合成により、図11の右側に示すような疑似発信源位置における誤差(電波強度の誤差)が得られる。
FIG. 11 is a diagram for explaining the operation of the error set synthesis unit 215 for a pseudo source. The error set synthesis unit 215 for the pseudo source synthesizes (adds the radio field intensity) the error set of each reference source position calculated earlier. At that time, the error set synthesis unit 215 for the pseudo source synthesizes the error set by the weighted average reflecting the position of the pseudo source and the position of the reference source. For example, in the upper left corner of FIG. 11, since the distance between the reference source and the pseudo source is long, the weight of the reference source is reduced. On the other hand, in the lowermost row on the left side, the distance between the reference source and the pseudo source is short, so the weight of the reference source is increased. By synthesizing such an error set, an error (error of radio field intensity) at the pseudo source position as shown on the right side of FIG. 11 can be obtained.
図11の例では、左側最下段に示すように、疑似発信源と参照発信源が近ければ、当該参照発信源の誤差マップと疑似発信源の誤差マップの類似度は高いはずである。対して、左側最上段に示すように、疑似発信源と参照発信源が遠ければ、当該参照発信源の誤差マップと疑似発信源の誤差マップの類似度は低いはずである。疑似発信源用誤差セット合成部215は、上記2種類の発信源の位置を考慮して、疑似発信源の誤差マップを生成する。
In the example of FIG. 11, as shown in the lowermost part on the left side, if the pseudo source and the reference source are close to each other, the degree of similarity between the error map of the reference source and the error map of the pseudo source should be high. On the other hand, as shown in the uppermost row on the left side, if the pseudo source and the reference source are far from each other, the similarity between the error map of the reference source and the error map of the pseudo source should be low. The error set synthesis unit 215 for the pseudo source generates an error map of the pseudo source in consideration of the positions of the above two types of sources.
逆距離加重法では、参照発信源位置と疑似発信源位置の間の距離の逆数に比例する重み係数が用いられる。例えば、距離の-1.5乗や-2乗に比例する重み係数が使用されてもよい。
In the inverse distance weighting method, a weighting coefficient proportional to the reciprocal of the distance between the reference source position and the pseudo source position is used. For example, a weighting factor proportional to the -1.5th power or -2nd power of the distance may be used.
誤差セットの合成手法の他の例として、はじめに、疑似発信源用誤差セット合成部215は、任意の2つの参照発信源に対して、各々の誤差セットの類似度が、2点間の距離に応じてどのように変化するかという空間相関モデルを作成する。その上で、疑似発信源用誤差セット合成部215は、当該空間相関モデルと整合するように、参照発信源位置と疑似発信源位置の間の距離に応じて重み係数を決定する。このように、空間相関を考慮した重み係数の決定方法としては、クリギングがある。
As another example of the error set synthesis method, first, the error set synthesis unit 215 for a pseudo source sets the similarity of each error set to the distance between two points for any two reference sources. Create a spatial correlation model of how it changes accordingly. Then, the error set synthesis unit 215 for the pseudo source determines the weighting coefficient according to the distance between the reference source position and the pseudo source position so as to be consistent with the spatial correlation model. As described above, there is kriging as a method of determining the weighting coefficient in consideration of the spatial correlation.
疑似計測値セット補正部216は、上記ステップS52で生成された疑似計測値セットを上記ステップS53で作成された誤差セットにより補正する(ステップS54)。具体的には、疑似計測値セット補正部216は、各疑似発信位置に関し、疑似計測値セットと誤差セットを加算することで、上記ステップS52で生成された疑似計測値セットを補正する。
The pseudo measurement value set correction unit 216 corrects the pseudo measurement value set generated in step S52 by the error set created in step S53 (step S54). Specifically, the pseudo measurement value set correction unit 216 corrects the pseudo measurement value set generated in step S52 by adding the pseudo measurement value set and the error set for each pseudo transmission position.
図12は、疑似計測値セット補正部216の動作を説明するための図である。疑似計測値セット補正部216が、疑似発信源位置の疑似計測値セットの電波強度とその誤差を加算すると、図12の右側に示すような補正後の疑似発信源位置での電波強度マップが得られる。当該電波強度マップは、疑似位置指紋データとなる。
FIG. 12 is a diagram for explaining the operation of the pseudo measurement value set correction unit 216. When the pseudo measurement value set correction unit 216 adds the radio field intensity of the pseudo measurement value set of the pseudo source position and its error, a radio field intensity map at the corrected pseudo source position as shown on the right side of FIG. 12 is obtained. Be done. The radio wave intensity map becomes pseudo position fingerprint data.
図13は、第1の実施形態に係る発信源位置推定システムの推定段階の動作の一例を示すフローチャートである。図13のフローチャートを参照して推定段階の動作について詳細に説明する。
FIG. 13 is a flowchart showing an example of the operation of the estimation stage of the source position estimation system according to the first embodiment. The operation of the estimation stage will be described in detail with reference to the flowchart of FIG.
はじめに、ステップS31と同様に、受信位置が取得される(ステップS61)。
First, the reception position is acquired in the same manner as in step S31 (step S61).
次に、入力部201は、ステップS41と同様に、信号処理手段により計算されたRSS計測値セットを取得する(ステップS62)。
Next, the input unit 201 acquires the RSS measurement value set calculated by the signal processing means in the same manner as in step S41 (step S62).
次に、位置推定部206は、ステップS34で訓練された位置推定器を用いて、未知発信源に対して得られたRSS計測値セットと、訓練用データに含まれるRSS計測値セットとを照合する(ステップS63)。
Next, the position estimation unit 206 collates the RSS measurement value set obtained for the unknown source with the RSS measurement value set included in the training data by using the position estimator trained in step S34. (Step S63).
その後、出力部207は、最も近い訓練用データのRSS計測値セットに対応する発信源位置を、未知発信源の推定位置として出力する(ステップS64)。
After that, the output unit 207 outputs the source position corresponding to the RSS measurement value set of the nearest training data as the estimated position of the unknown source (step S64).
なお、第1の実施形態では、多数の候補位置のなかから最も近い候補位置を1つ選んで出力する多クラス分類の手法を用いる場合を想定して説明した。しかし、本願開示では、発信源の位置座標を連続的な数値として取り扱って出力する回帰分析の手法を用いてもよい。
In the first embodiment, a case where a multi-class classification method of selecting and outputting one of the closest candidate positions from a large number of candidate positions is used has been described. However, in the disclosure of the present application, a regression analysis method may be used in which the position coordinates of the source are treated as continuous numerical values and output.
あるいは、未知発信源に対して得られたRSS計測値セットと、類似度の大きな複数の訓練用データのRSS計測値セットに対応する複数の候補位置に対して、類似度を重み係数とする加重平均を計算して推定位置として扱ってもよい。
Alternatively, a weighted coefficient of similarity is used for a plurality of candidate positions corresponding to the RSS measurement value set obtained for an unknown source and the RSS measurement value set of a plurality of training data having a large degree of similarity. The average may be calculated and treated as an estimated position.
多クラス分類の手法の例としては、ロジスティック回帰、ランダムフォレスト、サポートベクタマシン、決定木、ニューラルネットワーク、k近傍法などがある。回帰分析の手法の例としては、線形回帰、ロジスティック回帰、サポートベクタマシン、ランダムフォレスト、ニューラルネットワークなどがある。但し、本願開示が適用可能な位置推定方法は、上記の例示に限定されない。
Examples of multi-class classification methods include logistic regression, random forest, support vector machine, decision tree, neural network, k-nearest neighbor method, etc. Examples of regression analysis methods include linear regression, logistic regression, support vector machines, random forests, and neural networks. However, the position estimation method to which the disclosure of the present application is applicable is not limited to the above examples.
[効果の説明]
次に、第1の実施形態の効果について説明する。 [Explanation of effect]
Next, the effect of the first embodiment will be described.
次に、第1の実施形態の効果について説明する。 [Explanation of effect]
Next, the effect of the first embodiment will be described.
第1の実施形態では、参照発信源による訓練用データに基づき、疑似発信源による訓練データを作成することで、訓練データを拡張している。その結果、実際に計測して取得するRSS計測値セットの数を削減できる。その際、単純に複数の参照発信源位置に対応するRSS計測値セットを重ね合わせるのではなく、複数の参照発信源位置に対する計測値と予測値の誤差セットが重ね合わせられ、さらに、当該誤差セットを用いて予測値が補正されている。そのため、複数の候補位置から同時に電波が発信されたようなRSS計測値セットが合成されることを回避できる。即ち、システム運用時に複数の候補位置から同時に電波が発信されるようなことは希であり、このような希なRSS計測値セットを用いて学習モデルを生成しても当該学習モデルによる推定結果は精度の低いものとなる。
In the first embodiment, the training data is expanded by creating the training data by the pseudo source based on the training data by the reference source. As a result, the number of RSS measurement value sets that are actually measured and acquired can be reduced. At that time, instead of simply superimposing the RSS measurement value sets corresponding to the plurality of reference source positions, the error sets of the measured values and the predicted values for the plurality of reference source positions are superimposed, and further, the error sets are superimposed. The predicted value is corrected using. Therefore, it is possible to avoid synthesizing RSS measurement value sets in which radio waves are simultaneously transmitted from a plurality of candidate positions. That is, it is rare that radio waves are transmitted from a plurality of candidate positions at the same time during system operation, and even if a learning model is generated using such a rare RSS measurement value set, the estimation result by the learning model is obtained. The accuracy will be low.
また、第1の実施形態では、参照発信源を用いて実際に計測したRSS計測値セットに基づき伝搬モデルが作成されている。そのため、参照発信源位置の外側に疑似発信源を配置した場合に得られるRSS計測値セットが精度よく作成される。その結果、訓練段階での参照発信源位置よりも外側に未知の発信源が存在する場合であっても、推定精度の劣化度合いが低減される。
Further, in the first embodiment, a propagation model is created based on the RSS measurement value set actually measured using the reference source. Therefore, the RSS measurement value set obtained when the pseudo source is placed outside the reference source position is accurately created. As a result, the degree of deterioration of the estimation accuracy is reduced even when an unknown source exists outside the reference source position in the training stage.
[第2の実施形態]
次に、第2の実施形態について図面を参照して詳細に説明する。 [Second Embodiment]
Next, the second embodiment will be described in detail with reference to the drawings.
次に、第2の実施形態について図面を参照して詳細に説明する。 [Second Embodiment]
Next, the second embodiment will be described in detail with reference to the drawings.
[構成の説明]
図14は、第2の実施形態に係る位置推定装置40の処理構成(処理モジュール)の一例を示す図である。図14を参照すると、第2の実施形態に係る位置推定装置40は、第1の実施形態に係る位置推定装置40と比較して、複数波源疑似計測値セット合成部701をさらに有する点で異なる。ここで、複数波源疑似計測値セット合成部701は、疑似計測値合成部の一例である。 [Description of configuration]
FIG. 14 is a diagram showing an example of a processing configuration (processing module) of theposition estimation device 40 according to the second embodiment. Referring to FIG. 14, the position estimation device 40 according to the second embodiment is different from the position estimation device 40 according to the first embodiment in that it further has a plurality of wave source pseudo measurement value set synthesis unit 701. .. Here, the plurality of wave source pseudo measurement value set synthesis unit 701 is an example of the pseudo measurement value synthesis unit.
図14は、第2の実施形態に係る位置推定装置40の処理構成(処理モジュール)の一例を示す図である。図14を参照すると、第2の実施形態に係る位置推定装置40は、第1の実施形態に係る位置推定装置40と比較して、複数波源疑似計測値セット合成部701をさらに有する点で異なる。ここで、複数波源疑似計測値セット合成部701は、疑似計測値合成部の一例である。 [Description of configuration]
FIG. 14 is a diagram showing an example of a processing configuration (processing module) of the
[動作の説明]
複数波源疑似計測値セット合成部701は、概略つぎのように動作する。 [Explanation of operation]
The multi-wave source pseudo-measured value setsynthesis unit 701 operates as follows.
複数波源疑似計測値セット合成部701は、概略つぎのように動作する。 [Explanation of operation]
The multi-wave source pseudo-measured value set
複数波源疑似計測値セット合成部701は、互いに異なる地点から同時に複数の発信源が信号を送信している場合の訓練データを疑似的に作成するため、波源数が1のときのRSS計測値セットを重ね合わせて複数波源存在時のRSS計測値セットを作成する。重ね合わせるRSS計測値セットは、参照発信源によるRSS計測値セットでも、疑似発信源によるRSS計測値セットでも、いずれであってもよい。また、重ね合わせ方は、RSSの場合、線形表現での電力レベルを加算することが望ましい。ただし、加算後に対数表現に変換するなどの単調写像の処理を実施してもよい。
Multiple wave source pseudo measurement value set Since the synthesis unit 701 pseudo-creates training data when a plurality of sources are transmitting signals from different points at the same time, an RSS measurement value set when the number of wave sources is 1. To create an RSS measurement value set when multiple wave sources exist. The RSS measurement value set to be superimposed may be either an RSS measurement value set by a reference source or an RSS measurement value set by a pseudo source. Further, in the case of RSS, it is desirable to add the power level in a linear representation as the superposition method. However, monotonic mapping processing such as conversion to logarithmic representation after addition may be performed.
なお、第2の実施形態での訓練用データにおいて、1つのRSS計測値セットは、1つ以上の発信源位置と対応関係にある。したがって、位置推定部206は、1つ以上の発信源位置を出力できるよう、構成されている必要がある。例えば、1つのRSS計測値セットに対して、複数の候補位置を選んで出力する多ラベル分類の手法を用いることができる。
Note that in the training data in the second embodiment, one RSS measurement value set corresponds to one or more source positions. Therefore, the position estimation unit 206 needs to be configured so that it can output one or more source positions. For example, a multi-label classification method can be used in which a plurality of candidate positions are selected and output for one RSS measurement value set.
[効果の説明]
第2の実施形態の効果について説明する。第2の実施形態では、2以上の発信源が同時(実質的に同時)に電波を送信しているときのRSS計測値セットを作成し、当該RSS計測値セットを用いて学習を行う。その結果、領域内に複数の発信源が存在し、同時に電波が送信されている場合であっても、各々の発信源の位置を推定して出力できる。 [Explanation of effect]
The effect of the second embodiment will be described. In the second embodiment, an RSS measurement value set when two or more transmission sources are transmitting radio waves at the same time (substantially at the same time) is created, and learning is performed using the RSS measurement value set. As a result, even when there are a plurality of transmission sources in the area and radio waves are transmitted at the same time, the positions of the respective transmission sources can be estimated and output.
第2の実施形態の効果について説明する。第2の実施形態では、2以上の発信源が同時(実質的に同時)に電波を送信しているときのRSS計測値セットを作成し、当該RSS計測値セットを用いて学習を行う。その結果、領域内に複数の発信源が存在し、同時に電波が送信されている場合であっても、各々の発信源の位置を推定して出力できる。 [Explanation of effect]
The effect of the second embodiment will be described. In the second embodiment, an RSS measurement value set when two or more transmission sources are transmitting radio waves at the same time (substantially at the same time) is created, and learning is performed using the RSS measurement value set. As a result, even when there are a plurality of transmission sources in the area and radio waves are transmitted at the same time, the positions of the respective transmission sources can be estimated and output.
[第3の実施形態]
次に、第3の実施形態について図面を参照して詳細に説明する。 [Third Embodiment]
Next, the third embodiment will be described in detail with reference to the drawings.
次に、第3の実施形態について図面を参照して詳細に説明する。 [Third Embodiment]
Next, the third embodiment will be described in detail with reference to the drawings.
[構成の説明]
図15は、第3の実施形態に係る発信源位置推定システムの処理構成の一例を示す図である。図15を参照すると、第3の実施形態では、第1及び第2の実施形態の入力部201の代わりに、発信源からの信号を受信するM個のセンサ801-1~801-Mと、通信網802と、通信部803を有する点が異なる。 [Description of configuration]
FIG. 15 is a diagram showing an example of a processing configuration of the source position estimation system according to the third embodiment. Referring to FIG. 15, in the third embodiment, instead of theinput unit 201 of the first and second embodiments, M sensors 801-1 to 801-M that receive signals from the source are used. It differs from the communication network 802 in that it has a communication unit 803.
図15は、第3の実施形態に係る発信源位置推定システムの処理構成の一例を示す図である。図15を参照すると、第3の実施形態では、第1及び第2の実施形態の入力部201の代わりに、発信源からの信号を受信するM個のセンサ801-1~801-Mと、通信網802と、通信部803を有する点が異なる。 [Description of configuration]
FIG. 15 is a diagram showing an example of a processing configuration of the source position estimation system according to the third embodiment. Referring to FIG. 15, in the third embodiment, instead of the
複数のセンサ801-1~801-Mのそれぞれは、電波強度計測部804と、位置計測部805と、計測したデータを送信するための通信部806と、を含んで構成される。ここで、電波強度計測部804は、発信位置に依存した特徴量を計測する計測部の一例である。
Each of the plurality of sensors 801-1 to 801-M includes a radio wave intensity measuring unit 804, a position measuring unit 805, and a communication unit 806 for transmitting the measured data. Here, the radio wave intensity measuring unit 804 is an example of a measuring unit that measures a feature amount depending on the transmission position.
[動作の説明]
上記構成要素のそれぞれは概略つぎのように動作する。 [Explanation of operation]
Each of the above components operates as follows.
上記構成要素のそれぞれは概略つぎのように動作する。 [Explanation of operation]
Each of the above components operates as follows.
電波強度計測部804は、発信源からの電波を受信してそのRSSを計測する。位置計測部805は、自己位置を計測する。通信部806は、通信網802を介して、通信部803へ計測したデータを送信する。
The radio wave intensity measuring unit 804 receives the radio wave from the source and measures its RSS. The position measurement unit 805 measures its own position. The communication unit 806 transmits the measured data to the communication unit 803 via the communication network 802.
[効果の説明]
第3の実施形態の効果について説明する。第3の実施形態では、センサ801-1~801-MがRSSと自己位置を計測して、計測したデータが通信網802を介してデータ分類部202と接続されるように構成されているため、訓練及び推定のプロセスをオンラインで実施できる。 [Explanation of effect]
The effect of the third embodiment will be described. In the third embodiment, the sensors 801-1 to 801-M measure the RSS and the self-position, and the measured data is connected to thedata classification unit 202 via the communication network 802. , Training and estimation process can be carried out online.
第3の実施形態の効果について説明する。第3の実施形態では、センサ801-1~801-MがRSSと自己位置を計測して、計測したデータが通信網802を介してデータ分類部202と接続されるように構成されているため、訓練及び推定のプロセスをオンラインで実施できる。 [Explanation of effect]
The effect of the third embodiment will be described. In the third embodiment, the sensors 801-1 to 801-M measure the RSS and the self-position, and the measured data is connected to the
[第4の実施形態]
次に、第4の実施形態について図面を参照して詳細に説明する。 [Fourth Embodiment]
Next, the fourth embodiment will be described in detail with reference to the drawings.
次に、第4の実施形態について図面を参照して詳細に説明する。 [Fourth Embodiment]
Next, the fourth embodiment will be described in detail with reference to the drawings.
[構成の説明]
図16は、第4の実施形態に係る発信源位置推定システムの処理構成の一例を示す図である。図16を参照すると、第4の実施形態は、第3の実施形態の位置計測部805の代わりに、センサ位置保持部901とセンサ位置付加部902とを有する点が異なる。 [Description of configuration]
FIG. 16 is a diagram showing an example of a processing configuration of the source position estimation system according to the fourth embodiment. Referring to FIG. 16, the fourth embodiment is different in that it has a sensorposition holding unit 901 and a sensor position adding unit 902 instead of the position measuring unit 805 of the third embodiment.
図16は、第4の実施形態に係る発信源位置推定システムの処理構成の一例を示す図である。図16を参照すると、第4の実施形態は、第3の実施形態の位置計測部805の代わりに、センサ位置保持部901とセンサ位置付加部902とを有する点が異なる。 [Description of configuration]
FIG. 16 is a diagram showing an example of a processing configuration of the source position estimation system according to the fourth embodiment. Referring to FIG. 16, the fourth embodiment is different in that it has a sensor
[動作の説明]
上記構成要素のそれぞれは概略つぎのように動作する。 [Explanation of operation]
Each of the above components operates as follows.
上記構成要素のそれぞれは概略つぎのように動作する。 [Explanation of operation]
Each of the above components operates as follows.
センサ位置保持部901は、領域内に設置された各々のセンサの位置を保持している。センサ位置付加部902は、通信部803を介して得られる、センサ801-1~801-Mが計測したデータに、各々のセンサの位置を付加して出力する。
The sensor position holding unit 901 holds the position of each sensor installed in the area. The sensor position addition unit 902 adds the position of each sensor to the data measured by the sensors 801-1 to 801-M obtained via the communication unit 803 and outputs the data.
[効果の説明]
第4の実施形態の効果について説明する。第4の実施形態では、センサ801-1~801-Mが計測したRSS計測値セットに、センサ位置保持部901が保持するセンサ位置を付加してデータ分類部202と接続されるように構成されている。そのため、センサの位置計測部805が不要となり、センサを低コストに作成できる。 [Explanation of effect]
The effect of the fourth embodiment will be described. In the fourth embodiment, the sensor position held by the sensorposition holding unit 901 is added to the RSS measurement value set measured by the sensors 801-1 to 801-M so as to be connected to the data classification unit 202. ing. Therefore, the position measurement unit 805 of the sensor becomes unnecessary, and the sensor can be manufactured at low cost.
第4の実施形態の効果について説明する。第4の実施形態では、センサ801-1~801-Mが計測したRSS計測値セットに、センサ位置保持部901が保持するセンサ位置を付加してデータ分類部202と接続されるように構成されている。そのため、センサの位置計測部805が不要となり、センサを低コストに作成できる。 [Explanation of effect]
The effect of the fourth embodiment will be described. In the fourth embodiment, the sensor position held by the sensor
[第5の実施形態]
次に、第5の実施形態について図面を参照して詳細に説明する。 [Fifth Embodiment]
Next, the fifth embodiment will be described in detail with reference to the drawings.
次に、第5の実施形態について図面を参照して詳細に説明する。 [Fifth Embodiment]
Next, the fifth embodiment will be described in detail with reference to the drawings.
[構成の説明]
図17は、第5の実施形態に係る発信源位置推定システムの処理構成の一例を示す図である。図17を参照すると、第5の実施形態は、第4の実施形態と比較して、時間・気象計測部1001と、時間・気象ラベル付加部1002とを有する点が異なる。 [Description of configuration]
FIG. 17 is a diagram showing an example of a processing configuration of the source position estimation system according to the fifth embodiment. Referring to FIG. 17, the fifth embodiment is different from the fourth embodiment in that it has a time /weather measurement unit 1001 and a time / weather label addition unit 1002.
図17は、第5の実施形態に係る発信源位置推定システムの処理構成の一例を示す図である。図17を参照すると、第5の実施形態は、第4の実施形態と比較して、時間・気象計測部1001と、時間・気象ラベル付加部1002とを有する点が異なる。 [Description of configuration]
FIG. 17 is a diagram showing an example of a processing configuration of the source position estimation system according to the fifth embodiment. Referring to FIG. 17, the fifth embodiment is different from the fourth embodiment in that it has a time /
[動作の説明]
上記構成要素のそれぞれは概略つぎのように動作する。 [Explanation of operation]
Each of the above components operates as follows.
上記構成要素のそれぞれは概略つぎのように動作する。 [Explanation of operation]
Each of the above components operates as follows.
時間・気象計測部1001は、計測を行った年月日や時刻、曜日や祝祭日などのカレンダー情報や、その日の天候や気温や湿度を取得する。時間・気象ラベル付加部1002は、通信部803を介して得られる、センサ801-1~801-Mが計測したデータに、時間情報や気象情報を付加して出力する。
The time / weather measurement unit 1001 acquires calendar information such as the date and time of measurement, the day of the week, and public holidays, as well as the weather, temperature, and humidity of that day. The time / weather label addition unit 1002 adds time information and weather information to the data measured by the sensors 801-1 to 801-M obtained via the communication unit 803 and outputs the data.
[効果の説明]
第5の実施形態の効果について説明する。第5の実施形態では、センサ801-1~801-Mが計測したRSS計測値セットに、時間ラベルや気象ラベルが付与される。そのため、データ分類部202において、時間や気象に応じた分類が行える。すなわち、付与された時間ラベルと気象ラベルに応じた訓練と推定が行えるようになる。例えば、時間については、1年間を周期として、季節に応じて植生が変化するため、葉の生い茂った夏は電波が植生によって吸収されて減衰しやすく、葉の落ちた冬は電波を遮るものがなくなって遠距離まで届きやすい。 [Explanation of effect]
The effect of the fifth embodiment will be described. In the fifth embodiment, a time label or a weather label is added to the RSS measurement value set measured by the sensors 801-1 to 801-M. Therefore, thedata classification unit 202 can classify according to time and weather. That is, training and estimation can be performed according to the assigned time label and weather label. For example, with regard to time, since the vegetation changes according to the season with a cycle of one year, radio waves are easily absorbed and attenuated by the vegetation in the summer when the leaves are overgrown, and the radio waves are blocked in the winter when the leaves fall. It disappears and is easy to reach long distances.
第5の実施形態の効果について説明する。第5の実施形態では、センサ801-1~801-Mが計測したRSS計測値セットに、時間ラベルや気象ラベルが付与される。そのため、データ分類部202において、時間や気象に応じた分類が行える。すなわち、付与された時間ラベルと気象ラベルに応じた訓練と推定が行えるようになる。例えば、時間については、1年間を周期として、季節に応じて植生が変化するため、葉の生い茂った夏は電波が植生によって吸収されて減衰しやすく、葉の落ちた冬は電波を遮るものがなくなって遠距離まで届きやすい。 [Explanation of effect]
The effect of the fifth embodiment will be described. In the fifth embodiment, a time label or a weather label is added to the RSS measurement value set measured by the sensors 801-1 to 801-M. Therefore, the
また、1日周期や1週間周期では、ヒトの流動、それに伴う車両の増減がある。ヒトも車両も、電波にとっては吸収体や反射体となるため、ヒトの活動が盛んになる平日の日中は電波が飛びにくいなどの変化が想定される。同様に、雨天時は雨に電波が吸収されて減衰しやすく、晴天で乾燥した日は電波が飛びやすい。このような変化を考慮に入れた位置推定を行うことで、位置推定精度を改善することができる。なお、時間・気象計測部1001をセンサ801-1~801-Mが有していてもよい。
In addition, in the daily cycle and the weekly cycle, there is a flow of humans and the accompanying increase and decrease of vehicles. Since both humans and vehicles are absorbers and reflectors for radio waves, changes such as difficulty in flying radio waves during the daytime on weekdays when human activities are active are expected. Similarly, when it rains, radio waves are easily absorbed by the rain and attenuated, and when it is sunny and dry, the radio waves tend to fly. The position estimation accuracy can be improved by performing the position estimation in consideration of such a change. The sensors 801-1 to 801-M may have the time / weather measurement unit 1001.
続いて、位置推定装置40のハードウェアについて説明する。図18は、位置推定装置40のハードウェア構成の一例を示す図である。
Next, the hardware of the position estimation device 40 will be described. FIG. 18 is a diagram showing an example of the hardware configuration of the position estimation device 40.
位置推定装置40は、情報処理装置(所謂、コンピュータ)により構成可能であり、図18に例示する構成を備える。例えば、位置推定装置40は、プロセッサ311、メモリ312、入出力インターフェイス313及び通信インターフェイス314等を備える。上記プロセッサ311等の構成要素は内部バス等により接続され、相互に通信可能に構成されている。
The position estimation device 40 can be configured by an information processing device (so-called computer), and includes the configuration illustrated in FIG. For example, the position estimation device 40 includes a processor 311, a memory 312, an input / output interface 313, a communication interface 314, and the like. The components such as the processor 311 are connected by an internal bus or the like so that they can communicate with each other.
但し、図18に示す構成は、位置推定装置40のハードウェア構成を限定する趣旨ではない。位置推定装置40は、図示しないハードウェアを含んでもよいし、必要に応じて入出力インターフェイス313を備えていなくともよい。また、位置推定装置40に含まれるプロセッサ311等の数も図18の例示に限定する趣旨ではなく、例えば、複数のプロセッサ311が位置推定装置40に含まれていてもよい。
However, the configuration shown in FIG. 18 does not mean to limit the hardware configuration of the position estimation device 40. The position estimation device 40 may include hardware (not shown), or may not include an input / output interface 313 if necessary. Further, the number of processors 311 and the like included in the position estimation device 40 is not limited to the example of FIG. 18, and for example, a plurality of processors 311 may be included in the position estimation device 40.
プロセッサ311は、例えば、CPU(Central Processing Unit)、MPU(Micro Processing Unit)、DSP(Digital Signal Processor)等のプログラマブルなデバイスである。あるいは、プロセッサ311は、FPGA(Field Programmable Gate Array)、ASIC(Application Specific Integrated Circuit)等のデバイスであってもよい。プロセッサ311は、オペレーティングシステム(OS;Operating System)を含む各種プログラムを実行する。
The processor 311 is a programmable device such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a DSP (Digital Signal Processor). Alternatively, the processor 311 may be a device such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The processor 311 executes various programs including an operating system (OS; Operating System).
メモリ312は、RAM(Random Access Memory)、ROM(Read Only Memory)、HDD(Hard Disk Drive)、SSD(Solid State Drive)等である。メモリ312は、OSプログラム、アプリケーションプログラム、各種データを格納する。
The memory 312 is a RAM (RandomAccessMemory), a ROM (ReadOnlyMemory), an HDD (HardDiskDrive), an SSD (SolidStateDrive), or the like. The memory 312 stores an OS program, an application program, and various data.
入出力インターフェイス313は、図示しない表示装置や入力装置のインターフェイスである。表示装置は、例えば、液晶ディスプレイ等である。入力装置は、例えば、キーボードやマウス等のユーザ操作を受け付ける装置である。
The input / output interface 313 is an interface of a display device or an input device (not shown). The display device is, for example, a liquid crystal display or the like. The input device is, for example, a device that accepts user operations such as a keyboard and a mouse.
通信インターフェイス314は、他の装置と通信を行う回路、モジュール等である。例えば、通信インターフェイス314は、NIC(Network Interface Card)等を備える。
The communication interface 314 is a circuit, module, or the like that communicates with another device. For example, the communication interface 314 includes a NIC (Network Interface Card) and the like.
位置推定装置40の機能は、各種処理モジュールにより実現される。当該処理モジュールは、例えば、メモリ312に格納されたプログラムをプロセッサ311が実行することで実現される。また、当該プログラムは、コンピュータが読み取り可能な記憶媒体に記録することができる。記憶媒体は、半導体メモリ、ハードディスク、磁気記録媒体、光記録媒体等の非トランジェント(non-transitory)なものとすることができる。即ち、本発明は、コンピュータプログラム製品として具現することも可能である。また、上記プログラムは、ネットワークを介してダウンロードするか、あるいは、プログラムを記憶した記憶媒体を用いて、更新することができる。さらに、上記処理モジュールは、半導体チップにより実現されてもよい。
The function of the position estimation device 40 is realized by various processing modules. The processing module is realized, for example, by the processor 311 executing a program stored in the memory 312. The program can also be recorded on a computer-readable storage medium. The storage medium may be a non-transient such as a semiconductor memory, a hard disk, a magnetic recording medium, or an optical recording medium. That is, the present invention can also be embodied as a computer program product. In addition, the program can be downloaded via a network or updated using a storage medium in which the program is stored. Further, the processing module may be realized by a semiconductor chip.
[変形例]
なお、上記実施形態にて説明した発信源位置推定システムの構成、動作等は例示であって、システムの構成等を限定する趣旨ではない。例えば、受信装置10と信号処理装置20が統合されてこれらの機能が単一の装置により実現されていてもよい。 [Modification example]
It should be noted that the configuration, operation, and the like of the source position estimation system described in the above embodiment are examples, and are not intended to limit the system configuration and the like. For example, the receivingdevice 10 and the signal processing device 20 may be integrated and these functions may be realized by a single device.
なお、上記実施形態にて説明した発信源位置推定システムの構成、動作等は例示であって、システムの構成等を限定する趣旨ではない。例えば、受信装置10と信号処理装置20が統合されてこれらの機能が単一の装置により実現されていてもよい。 [Modification example]
It should be noted that the configuration, operation, and the like of the source position estimation system described in the above embodiment are examples, and are not intended to limit the system configuration and the like. For example, the receiving
また、上述の説明で用いた複数のフローチャートでは、複数の工程(処理)が順番に記載されているが、各実施形態で実行される工程の実行順序は、その記載の順番に制限されない。各実施形態では、例えば各処理を並行して実行する等、図示される工程の順番を内容的に支障のない範囲で変更することができる。また、上述の各実施形態は、内容が相反しない範囲で組み合わせることができる。
Further, in the plurality of flowcharts used in the above description, a plurality of steps (processes) are described in order, but the execution order of the steps executed in each embodiment is not limited to the order of description. In each embodiment, the order of the illustrated steps can be changed within a range that does not hinder the contents, for example, each process is executed in parallel. In addition, the above-described embodiments can be combined as long as the contents do not conflict with each other.
上記の説明により、本発明の産業上の利用可能性は明らかであるが、本発明は、不法・違法な電波発信源の位置を推定して適切な処置を行うといった用途に好適である。また、電波の発信器(ビーコン)を備えたヒトやモノの位置を探知・追跡するといった用途にも好適である。
Although the industrial applicability of the present invention is clear from the above description, the present invention is suitable for applications such as estimating the position of an illegal / illegal radio wave transmission source and taking appropriate measures. It is also suitable for applications such as detecting and tracking the position of a person or object equipped with a radio wave transmitter (beacon).
上記の実施形態の一部又は全部は、以下の付記のようにも記載され得るが、以下には限られない。
[付記1]
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する、予測モデル生成部(101、211)と、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する、誤差群算出部(102、212)と、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する、誤差合成部(103、215)と、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する、疑似計測値補正部(104、216)と、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する位置推定部(105、206)と、
を備える、位置推定装置(40、100)。
[付記2]
前記誤差合成部(103、215)は、前記任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて決定される重み係数を用いて前記誤差群を加重平均し、前記合成誤差を計算する、付記1に記載の位置推定装置(40、100)。
[付記3]
前記既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と前記補正後の疑似発信源からの特徴量の予測値に基づき、前記発信位置に依存した特徴量と正解位置の対応関係を学習し学習モデルを生成する、学習部(205)をさらに備える、付記1又は2に記載の位置推定装置(40、100)。
[付記4]
前記重み係数は、前記疑似発信源と前記参照発信源の間の距離のべき乗で表現される、付記2に記載の位置推定装置(40、100)。
[付記5]
前記重み係数は、任意の2つの参照発信源に対して、2点間の距離に対する誤差の類似度の空間相関モデルを作成し、当該空間相関モデルと整合するように、参照発信源と疑似発信源の間の距離に応じて決定される、付記2に記載の位置推定装置(40、100)。
[付記6]
異なる発信源位置に対応する前記計測値又は前記疑似発信源による疑似計測値を1以上合成して出力する、疑似計測値合成部(701)をさらに備える、付記1乃至5のいずれか一つに記載の位置推定装置(40、100)。
[付記7]
前記発信位置に依存した特徴量を計測する計測部を備えるセンサとネットワークを介して接続された通信部(803)をさらに備える、付記1乃至6のいずれか一つに記載の位置推定装置(40、100)。
[付記8]
前記特徴量は、電波強度である付記1乃至7のいずれか一つに記載の位置推定装置(40、100)。
[付記9]
領域内に設置された各々の前記センサの位置を保持する、センサ位置保持部(901)と、
前記通信部(803)を介して得られる、前記センサが計測したデータに前記センサの位置を付加して出力する、センサ位置付加部(902)と、をさらに備える、付記7に記載の位置推定装置(40、100)。
[付記10]
計測を行った時間に関する時間情報、又は、計測を行った日の気象に関する気象情報を取得する時間・気象計測部(1001)と、
前記通信部を介して得られる前記センサが計測したデータに、前記時間情報又は気象情報を付加する、時間・気象ラベル付加部(1002)と、
をさらに備える、付記7に記載の位置推定装置(40、100)。
[付記11]
位置推定装置(40、100)において、
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成するステップと、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出するステップと、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力するステップと、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正するステップと、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定するステップと、
を含む、位置推定方法。
[付記12]
位置推定装置(40、100)に搭載されたコンピュータに、
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する処理と、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する処理と、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する処理と、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する処理と、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する処理と、
を実行させるプログラム。
なお、付記11の形態及び付記12の形態は、付記1の形態と同様に、付記2の形態~付記10の形態に展開することが可能である。 Some or all of the above embodiments may also be described, but not limited to:
[Appendix 1]
Prediction model generation units (101, 211) that generate a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The error group calculation unit (102, 212), which calculates the error group by calculating with respect to the reference source position of
An error synthesizing unit (103, 215) that synthesizes the error group according to the positional relationship between the pseudo transmission source at an arbitrary position and each of the reference transmission sources and outputs the combined error.
Pseudo-measured value correction units (104, 216) that correct the predicted value of the feature amount by the prediction model when the signal from the pseudo-source is received by using the synthesis error.
A position estimation unit that estimates the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. (105, 206) and
Position estimation devices (40, 100).
[Appendix 2]
The error synthesizing unit (103, 215) weighted and averages the error group using a weighting coefficient determined according to the positional relationship between the pseudo source at the arbitrary position and each of the reference sources, and the combined error. The position estimation device (40, 100) according toAppendix 1 for calculating the above.
[Appendix 3]
Based on the measured value of the feature amount acquired based on the signal from the reference source of the known position and the predicted value of the feature amount from the pseudo source after the correction, the feature amount and the correct answer position depending on the transmission position The position estimation device (40, 100) according toAppendix 1 or 2, further comprising a learning unit (205) that learns correspondences and generates a learning model.
[Appendix 4]
The position estimation device (40, 100) according to Appendix 2, wherein the weighting factor is expressed as a power of the distance between the pseudo source and the reference source.
[Appendix 5]
The weighting coefficient creates a spatial correlation model of the similarity of the error with respect to the distance between two points for any two reference sources, and pseudo-transmits with the reference source so as to be consistent with the spatial correlation model. The position estimation device (40, 100) according to Appendix 2, which is determined according to the distance between the sources.
[Appendix 6]
In any one ofAppendix 1 to 5, further including a pseudo-measurement value synthesizer (701) that synthesizes and outputs one or more of the measurement values corresponding to different source positions or the pseudo-measurement values by the pseudo-source. The position estimation device (40, 100) described.
[Appendix 7]
The position estimation device (40) according to any one ofSupplementary note 1 to 6, further comprising a sensor including a measurement unit for measuring a feature amount depending on the transmission position and a communication unit (803) connected via a network. , 100).
[Appendix 8]
The position estimation device (40, 100) according to any one ofSupplementary notes 1 to 7, wherein the feature amount is a radio wave intensity.
[Appendix 9]
A sensor position holding unit (901) that holds the position of each of the sensors installed in the area, and
The position estimation according to Appendix 7, further comprising a sensor position addition unit (902) that adds the position of the sensor to the data measured by the sensor and outputs the data obtained via the communication unit (803). Equipment (40, 100).
[Appendix 10]
The time / weather measurement unit (1001) that acquires time information related to the time when the measurement was performed or weather information related to the weather on the day when the measurement was performed.
A time / weather label addition unit (1002) that adds the time information or the weather information to the data measured by the sensor obtained via the communication unit.
The position estimation device (40, 100) according to Appendix 7, further comprising.
[Appendix 11]
In the position estimation device (40, 100)
A step to generate a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The step of calculating the error group by calculating for the reference source position of
A step of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
A step of correcting the predicted value of the feature amount by the prediction model when the signal from the pseudo transmission source is received by using the synthesis error, and
A step of estimating the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
Location estimation methods, including.
[Appendix 12]
In the computer mounted on the position estimation device (40, 100),
Processing to generate a prediction model that predicts the feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position,
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The process of calculating the error group by calculating for the reference source position of
A process of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
A process of correcting the predicted value of the feature amount by the prediction model when a signal from the pseudo transmission source is received by using the synthesis error, and
A process of estimating the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
A program that executes.
Note that the form of Appendix 11 and the form of Appendix 12 can be expanded to the forms of Appendix 2 to the form ofAppendix 10 in the same manner as the form of Appendix 1.
[付記1]
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する、予測モデル生成部(101、211)と、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する、誤差群算出部(102、212)と、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する、誤差合成部(103、215)と、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する、疑似計測値補正部(104、216)と、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する位置推定部(105、206)と、
を備える、位置推定装置(40、100)。
[付記2]
前記誤差合成部(103、215)は、前記任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて決定される重み係数を用いて前記誤差群を加重平均し、前記合成誤差を計算する、付記1に記載の位置推定装置(40、100)。
[付記3]
前記既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と前記補正後の疑似発信源からの特徴量の予測値に基づき、前記発信位置に依存した特徴量と正解位置の対応関係を学習し学習モデルを生成する、学習部(205)をさらに備える、付記1又は2に記載の位置推定装置(40、100)。
[付記4]
前記重み係数は、前記疑似発信源と前記参照発信源の間の距離のべき乗で表現される、付記2に記載の位置推定装置(40、100)。
[付記5]
前記重み係数は、任意の2つの参照発信源に対して、2点間の距離に対する誤差の類似度の空間相関モデルを作成し、当該空間相関モデルと整合するように、参照発信源と疑似発信源の間の距離に応じて決定される、付記2に記載の位置推定装置(40、100)。
[付記6]
異なる発信源位置に対応する前記計測値又は前記疑似発信源による疑似計測値を1以上合成して出力する、疑似計測値合成部(701)をさらに備える、付記1乃至5のいずれか一つに記載の位置推定装置(40、100)。
[付記7]
前記発信位置に依存した特徴量を計測する計測部を備えるセンサとネットワークを介して接続された通信部(803)をさらに備える、付記1乃至6のいずれか一つに記載の位置推定装置(40、100)。
[付記8]
前記特徴量は、電波強度である付記1乃至7のいずれか一つに記載の位置推定装置(40、100)。
[付記9]
領域内に設置された各々の前記センサの位置を保持する、センサ位置保持部(901)と、
前記通信部(803)を介して得られる、前記センサが計測したデータに前記センサの位置を付加して出力する、センサ位置付加部(902)と、をさらに備える、付記7に記載の位置推定装置(40、100)。
[付記10]
計測を行った時間に関する時間情報、又は、計測を行った日の気象に関する気象情報を取得する時間・気象計測部(1001)と、
前記通信部を介して得られる前記センサが計測したデータに、前記時間情報又は気象情報を付加する、時間・気象ラベル付加部(1002)と、
をさらに備える、付記7に記載の位置推定装置(40、100)。
[付記11]
位置推定装置(40、100)において、
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成するステップと、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出するステップと、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力するステップと、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正するステップと、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定するステップと、
を含む、位置推定方法。
[付記12]
位置推定装置(40、100)に搭載されたコンピュータに、
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する処理と、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する処理と、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する処理と、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する処理と、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する処理と、
を実行させるプログラム。
なお、付記11の形態及び付記12の形態は、付記1の形態と同様に、付記2の形態~付記10の形態に展開することが可能である。 Some or all of the above embodiments may also be described, but not limited to:
[Appendix 1]
Prediction model generation units (101, 211) that generate a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The error group calculation unit (102, 212), which calculates the error group by calculating with respect to the reference source position of
An error synthesizing unit (103, 215) that synthesizes the error group according to the positional relationship between the pseudo transmission source at an arbitrary position and each of the reference transmission sources and outputs the combined error.
Pseudo-measured value correction units (104, 216) that correct the predicted value of the feature amount by the prediction model when the signal from the pseudo-source is received by using the synthesis error.
A position estimation unit that estimates the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. (105, 206) and
Position estimation devices (40, 100).
[Appendix 2]
The error synthesizing unit (103, 215) weighted and averages the error group using a weighting coefficient determined according to the positional relationship between the pseudo source at the arbitrary position and each of the reference sources, and the combined error. The position estimation device (40, 100) according to
[Appendix 3]
Based on the measured value of the feature amount acquired based on the signal from the reference source of the known position and the predicted value of the feature amount from the pseudo source after the correction, the feature amount and the correct answer position depending on the transmission position The position estimation device (40, 100) according to
[Appendix 4]
The position estimation device (40, 100) according to Appendix 2, wherein the weighting factor is expressed as a power of the distance between the pseudo source and the reference source.
[Appendix 5]
The weighting coefficient creates a spatial correlation model of the similarity of the error with respect to the distance between two points for any two reference sources, and pseudo-transmits with the reference source so as to be consistent with the spatial correlation model. The position estimation device (40, 100) according to Appendix 2, which is determined according to the distance between the sources.
[Appendix 6]
In any one of
[Appendix 7]
The position estimation device (40) according to any one of
[Appendix 8]
The position estimation device (40, 100) according to any one of
[Appendix 9]
A sensor position holding unit (901) that holds the position of each of the sensors installed in the area, and
The position estimation according to Appendix 7, further comprising a sensor position addition unit (902) that adds the position of the sensor to the data measured by the sensor and outputs the data obtained via the communication unit (803). Equipment (40, 100).
[Appendix 10]
The time / weather measurement unit (1001) that acquires time information related to the time when the measurement was performed or weather information related to the weather on the day when the measurement was performed.
A time / weather label addition unit (1002) that adds the time information or the weather information to the data measured by the sensor obtained via the communication unit.
The position estimation device (40, 100) according to Appendix 7, further comprising.
[Appendix 11]
In the position estimation device (40, 100)
A step to generate a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The step of calculating the error group by calculating for the reference source position of
A step of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
A step of correcting the predicted value of the feature amount by the prediction model when the signal from the pseudo transmission source is received by using the synthesis error, and
A step of estimating the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
Location estimation methods, including.
[Appendix 12]
In the computer mounted on the position estimation device (40, 100),
Processing to generate a prediction model that predicts the feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position,
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The process of calculating the error group by calculating for the reference source position of
A process of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
A process of correcting the predicted value of the feature amount by the prediction model when a signal from the pseudo transmission source is received by using the synthesis error, and
A process of estimating the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
A program that executes.
Note that the form of Appendix 11 and the form of Appendix 12 can be expanded to the forms of Appendix 2 to the form of
以上、本発明の実施形態を説明したが、本発明はこれらの実施形態に限定されるものではない。これらの実施形態は例示にすぎないということ、及び、本発明のスコープ及び精神から逸脱することなく様々な変形が可能であるということは、当業者に理解されるであろう。
Although the embodiments of the present invention have been described above, the present invention is not limited to these embodiments. It will be appreciated by those skilled in the art that these embodiments are merely exemplary and that various modifications are possible without departing from the scope and spirit of the invention.
この出願は、2019年4月12日に出願された日本出願特願2019-076361を基礎とする優先権を主張し、その開示の全てをここに取り込む。
This application claims priority based on Japanese application Japanese Patent Application No. 2019-076361 filed on April 12, 2019, and incorporates all of its disclosures herein.
10、10-1~10-M 受信装置
20、20-1~20-M 信号処理装置
30 記憶装置
40、100 位置推定装置
101 予測モデル生成部
102 誤差群算出部
103 誤差合成部
104 疑似計測値補正部
105、206 位置推定部
201 入力部
202 データ分類部
203 訓練データ拡張部
204 訓練データ結合部
205 学習部
207 出力部
211 伝搬モデル生成部
212 参照発信源誤差セット作成部
213 疑似発信源位置決定部
214 モデルベース疑似計測値セット生成部
215 疑似発信源用誤差セット合成部
216 疑似計測値セット補正部
311 プロセッサ
312 メモリ
313 入出力インターフェイス
314 通信インターフェイス
701 複数波源疑似計測値セット合成部
801-1~801-M センサ
802 通信網
803、806 通信部
804 電波強度計測部
805 位置計測部
901 センサ位置保持部
902 センサ位置付加部
1001 時間・気象計測部
1002 時間・気象ラベル付加部
10, 10-1 to 10-M receiver 20, 20-1 to 20-M signal processing device 30 storage device 40, 100 position estimation device 101 prediction model generation unit 102 error group calculation unit 103 error synthesis unit 104 pseudo measurement value Correction unit 105, 206 Position estimation unit 201 Input unit 202 Data classification unit 203 Training data expansion unit 204 Training data coupling unit 205 Learning unit 207 Output unit 211 Propagation model generation unit 212 Reference source error set creation unit 213 Pseudo source position determination Unit 214 Model-based pseudo measurement value set generation unit 215 Pseudo source error set synthesis unit 216 Pseudo measurement value set correction unit 311 Processor 312 Memory 313 Input / output interface 314 Communication interface 701 Multiple wave source pseudo measurement value set synthesis unit 801-1 801-M Sensor 802 Communication network 803, 806 Communication unit 804 Radio strength measurement unit 805 Position measurement unit 901 Sensor position holding unit 902 Sensor position addition unit 1001 Time / weather measurement unit
1002 hours / weather label addition part
20、20-1~20-M 信号処理装置
30 記憶装置
40、100 位置推定装置
101 予測モデル生成部
102 誤差群算出部
103 誤差合成部
104 疑似計測値補正部
105、206 位置推定部
201 入力部
202 データ分類部
203 訓練データ拡張部
204 訓練データ結合部
205 学習部
207 出力部
211 伝搬モデル生成部
212 参照発信源誤差セット作成部
213 疑似発信源位置決定部
214 モデルベース疑似計測値セット生成部
215 疑似発信源用誤差セット合成部
216 疑似計測値セット補正部
311 プロセッサ
312 メモリ
313 入出力インターフェイス
314 通信インターフェイス
701 複数波源疑似計測値セット合成部
801-1~801-M センサ
802 通信網
803、806 通信部
804 電波強度計測部
805 位置計測部
901 センサ位置保持部
902 センサ位置付加部
1001 時間・気象計測部
1002 時間・気象ラベル付加部
10, 10-1 to 10-
1002 hours / weather label addition part
Claims (12)
- 任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する、予測モデル生成部と、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する、誤差群算出部と、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する、誤差合成部と、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する、疑似計測値補正部と、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する位置推定部と、
を備える、位置推定装置。 A prediction model generator that generates a prediction model that predicts features depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount related to the reference source at the known position calculated using the prediction model. The error group calculation unit, which calculates the error group by calculating for the reference source position of
An error synthesizer that synthesizes the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputs it as a composite error.
A pseudo-measurement value correction unit that corrects the predicted value of the feature amount by the prediction model when a signal from the pseudo-source is received by using the synthesis error.
A position estimation unit that estimates the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source. When,
A position estimation device. - 前記誤差合成部は、前記任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて決定される重み係数を用いて前記誤差群を加重平均し、前記合成誤差を計算する、請求項1に記載の位置推定装置。 The error synthesizing unit calculates the combined error by weighted averaging the error group using a weighting coefficient determined according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources. Item 1. The position estimation device according to item 1.
- 前記既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と前記補正後の疑似発信源からの特徴量の予測値に基づき、前記発信位置に依存した特徴量と正解位置の対応関係を学習し学習モデルを生成する、学習部をさらに備える、請求項1又は2に記載の位置推定装置。 Based on the measured value of the feature amount acquired based on the signal from the reference source of the known position and the predicted value of the feature amount from the pseudo source after the correction, the feature amount and the correct answer position depending on the transmission position The position estimation device according to claim 1 or 2, further comprising a learning unit that learns correspondences and generates a learning model.
- 前記重み係数は、前記疑似発信源と前記参照発信源の間の距離のべき乗で表現される、請求項2に記載の位置推定装置。 The position estimation device according to claim 2, wherein the weighting coefficient is expressed by a power of the distance between the pseudo source and the reference source.
- 前記重み係数は、任意の2つの参照発信源に対して、2点間の距離に対する誤差の類似度の空間相関モデルを作成し、当該空間相関モデルと整合するように、参照発信源と疑似発信源の間の距離に応じて決定される、請求項2に記載の位置推定装置。 The weighting coefficient creates a spatial correlation model of the similarity of the error with respect to the distance between two points for any two reference sources, and pseudo-transmits with the reference source so as to be consistent with the spatial correlation model. The position estimation device according to claim 2, which is determined according to the distance between the sources.
- 異なる発信源位置に対応する前記計測値又は前記疑似発信源による疑似計測値を1以上合成して出力する、疑似計測値合成部をさらに備える、請求項1乃至5のいずれか一項に記載の位置推定装置。 The one according to any one of claims 1 to 5, further comprising a pseudo-measurement value synthesizer that synthesizes and outputs one or more of the measurement values corresponding to different source positions or the pseudo-measurement values by the pseudo-source. Position estimator.
- 前記発信位置に依存した特徴量を計測する計測部を備えるセンサとネットワークを介して接続された通信部をさらに備える、請求項1乃至6のいずれか一項に記載の位置推定装置。 The position estimation device according to any one of claims 1 to 6, further comprising a sensor including a measuring unit for measuring a feature amount depending on the transmission position and a communication unit connected via a network.
- 前記特徴量は、電波強度である請求項1乃至7のいずれか一項に記載の位置推定装置。 The position estimation device according to any one of claims 1 to 7, wherein the feature amount is a radio wave intensity.
- 領域内に設置された各々の前記センサの位置を保持する、センサ位置保持部と、
前記通信部を介して得られる、前記センサが計測したデータに前記センサの位置を付加して出力する、センサ位置付加部と、をさらに備える、請求項7に記載の位置推定装置。 A sensor position holding unit that holds the position of each of the sensors installed in the area,
The position estimation device according to claim 7, further comprising a sensor position addition unit that adds the position of the sensor to the data measured by the sensor and outputs the data obtained via the communication unit. - 計測を行った時間に関する時間情報、又は、計測を行った日の気象に関する気象情報を取得する時間・気象計測部と、
前記通信部を介して得られる前記センサが計測したデータに、前記時間情報又は気象情報を付加する、時間・気象ラベル付加部と、
をさらに備える、請求項7に記載の位置推定装置。 The time / weather measurement unit that acquires time information related to the time of measurement or weather information related to the weather on the day of measurement,
A time / weather label addition unit that adds the time information or the weather information to the data measured by the sensor obtained via the communication unit.
7. The position estimation device according to claim 7. - 位置推定装置において、
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成するステップと、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出するステップと、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力するステップと、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正するステップと、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定するステップと、
を含む、位置推定方法。 In the position estimation device
A step to generate a prediction model that predicts a feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position.
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The step of calculating the error group by calculating for the reference source position of
A step of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
A step of correcting the predicted value of the feature amount by the prediction model when the signal from the pseudo transmission source is received by using the synthesis error, and
A step of estimating the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
Location estimation methods, including. - 位置推定装置に搭載されたコンピュータに、
任意位置の発信源からの信号を特定の位置で受信したときに得られる、発信位置に依存した特徴量を予測する予測モデルを生成する処理と、
既知位置の参照発信源からの信号に基づき取得された特徴量の計測値と、前記予測モデルを用いて算出された、前記既知位置の参照発信源に関する特徴量の予測値と、の誤差を複数の参照発信源位置に対して計算することで誤差群を算出する処理と、
任意位置の疑似発信源と前記参照発信源各々との位置関係に応じて前記誤差群を合成し、合成誤差として出力する処理と、
前記疑似発信源からの信号を受信したときの前記予測モデルによる特徴量の予測値を、前記合成誤差を用いて補正する処理と、
前記発信位置に依存した特徴量の計測値を、少なくとも前記補正後の疑似発信源からの特徴量の予測値に基づき学習された学習モデルに入力することで、発信源位置を推定する処理と、
を実行させるプログラム。
On the computer installed in the position estimation device,
Processing to generate a prediction model that predicts the feature amount depending on the transmission position, which is obtained when a signal from a source at an arbitrary position is received at a specific position,
Multiple errors between the measured value of the feature amount acquired based on the signal from the reference source at the known position and the predicted value of the feature amount for the reference source at the known position calculated using the prediction model. The process of calculating the error group by calculating for the reference source position of
A process of synthesizing the error group according to the positional relationship between the pseudo source at an arbitrary position and each of the reference sources and outputting it as a combined error.
A process of correcting the predicted value of the feature amount by the prediction model when a signal from the pseudo transmission source is received by using the synthesis error, and
A process of estimating the source position by inputting the measured value of the feature amount depending on the transmission position into a learning model learned based on at least the predicted value of the feature amount from the corrected pseudo source.
A program that executes.
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