Estimation method, estimation device, and recording medium

Abstract

An estimation method, an estimation device, and a storage medium capable of evaluating the reliability of an estimation result of the position of a living body existing in a target space using a wireless signal. The method comprises: an extraction step (S3) of extracting a living body component in each of N receiving antenna elements from a plurality of calculated complex transfer functions; a correlation matrix calculation step (S4) of calculating a correlation matrix based on each extracted living body component; a calculation step (S5) of calculating one or more eigenvalues ​​of the calculated correlation matrix; a reliability estimation step (S6) of estimating the reliability of an estimation result when estimating the position or direction of a living body existing in the target space using information on the number of living bodies in the target space and one or more calculated eigenvalues; and an estimation step (S7) of estimating the position or direction of the living body according to a predetermined method based on the correlation matrix according to the reliability of the estimation result.

Description

Technical Field

[0001] The present disclosure relates to an estimation method, an estimation device, and a recording medium using a wireless signal. Background Art

[0002] There are technologies for detecting objects using wireless signals (see, for example, Patent Documents 1 and 2). For example, Patent Document 1 discloses a technology for obtaining an autocorrelation matrix of a received signal and obtaining the number of target objects based on the magnitude of the eigenvalue of the autocorrelation matrix. In addition, for example, Patent Document 2 discloses a technology for obtaining the number of target objects by performing eigenvalue decomposition on the correlation matrix of a received signal and counting the number of eigenvalues ​​above a threshold.

[0003] Furthermore, for example, Patent Document 3 discloses a technique for measuring the position or state of a person to be detected by analyzing a component including a Doppler shift using Fourier transform.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2009-281775

[0007] Patent Document 2: Japanese Patent Application Publication No. 2000-171550

[0008] Patent Document 3: Japanese Patent Application Publication No. 2015-117972 Summary of the invention

[0009] Problems to be solved by the invention

[0010] However, the technologies disclosed in Patent Documents 1 and 2 are device position detection technologies, so there is a problem that the position of the biological body cannot be estimated. In addition, in the technology disclosed in Patent Document 3, there is a situation where an instantaneous positioning error occurs due to external noise, etc., and there is a problem that it is impossible to know how reliable the positioning result is.

[0011] The present disclosure has been made in view of the above circumstances, and an object of the present disclosure is to provide an estimation method, an estimation device, and a recording medium capable of evaluating the reliability of an estimation result of the position of a living body existing in a target space, etc., using wireless signals.

[0012] Means for solving problems

[0013] In order to achieve the above-mentioned purpose, an estimation method of a technical solution of the present disclosure is an estimation method of an estimation device, wherein the estimation device uses a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements to estimate the position or direction of a biological body existing in an object space, wherein N is a natural number greater than 2, and the estimation method includes: a transfer function calculation step, in which a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements are calculated based on received signals received by each of the N receiving antenna elements during a specified period; and an extraction step, in which the N biological components caused by the influence of the biological body are extracted from the plurality of complex transfer functions calculated in the transfer function calculation step. A biological component in each of the N receiving antenna elements; a correlation matrix calculation step, calculating a correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extraction step; a calculation step, calculating one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation step; a reliability estimation step, estimating the reliability of an estimation result when estimating the position or direction of the biological body using biological quantity information representing the value of the number of biological bodies existing in the object space and the one or more eigenvalues ​​calculated in the calculation step; and an estimation step, estimating the position or direction of the biological body by a prescribed method based on the correlation matrix according to the reliability of the estimation result.

[0014] In addition, these overall or specific technical solutions can also be implemented by systems, devices, methods, integrated circuits, computer programs or computer-readable CD-ROMs and other recording media, or by any combination of systems, methods, integrated circuits, computer programs and recording media.

[0015] Effects of the Invention

[0016] According to the estimation method and the like of the present disclosure, it is possible to evaluate the reliability of the estimation result of the position of a living body existing in a target space and the like using wireless signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a block diagram showing an example of the configuration of the estimation device in the first embodiment.

[0018] Figure 2 It is schematically indicated Figure 1 A conceptual diagram of the propagation of a transmission wave in the estimation device shown.

[0019] Figure 3 This is a diagram showing an example of eigenvalue distribution in the first embodiment.

[0020] Figure 4This is a diagram showing an example of ratio information calculated from the eigenvalue distribution in the first embodiment.

[0021] Figure 5 This is a block diagram showing an example of the configuration of the reliability estimation unit in the first embodiment.

[0022] Figure 6 This is a diagram showing another example of the eigenvalue distribution in the first embodiment.

[0023] Figure 7 This is a diagram showing another example of ratio information calculated from the eigenvalue distribution in the first embodiment.

[0024] Figure 8 This is a flowchart showing an example of the operation of the estimation device in the first embodiment.

[0025] Fig. 9 This is a flowchart showing another example of the operation of the estimation device in the first embodiment.

[0026] Fig.10 This is a flowchart showing an example of detailed processing of step S6 in the first embodiment.

[0027] Fig.11 This is a diagram showing an example of positioning error distribution when a multi-carrier signal in a modified example of the first embodiment is used.

[0028] Fig. 12A It means Fig.11 The distribution diagram shown shows whether the ratio of the first eigenvalue and the second eigenvalue at the same timing of the positioning error distribution is greater than a threshold value.

[0029] Fig. 12B It means Fig.11 The distribution diagram shown is a distribution diagram showing whether the distribution of the ratio of the second eigenvalue and the third eigenvalue at the same timing of the positioning error distribution is equal to or greater than a threshold value.

[0030] Fig.13 This is a block diagram showing an example of the configuration of an estimation device in the second embodiment.

[0031] Fig.14 This is a block diagram showing an example of the configuration of an estimation device in the third embodiment.

[0032] Fig.15 This is a diagram showing an example of a graph of the evaluation function in the third embodiment.

[0033] Description of Reference Numerals

[0034] 1.1A, 1B Estimation device

[0035] 10, 10A transmitter

[0036] 11 Receiver

[0037] 12. Biometric information calculation unit

[0038] 13 Computing Department

[0039] 14 Storage

[0040] 15.15B Reliability Estimation Department

[0041] 16.16B Direction estimation unit

[0042] 50 Organisms

[0043] 101, 101A Transmitting antenna unit

[0044] 102 Sending Department

[0045] 111 Receiving antenna unit

[0046] 112 Receiving Department

[0047] 121 Complex transfer function calculation unit

[0048] 122 Extraction Department

[0049] 123 Correlation matrix calculation unit

[0050] 151 Eigenvalue distribution calculation unit

[0051] 152 Feature determination unit

[0052] 153 Reliability Determination Department DETAILED DESCRIPTION

[0053] (Insights that form the basis of this disclosure)

[0054] The inventors have conducted a detailed study of the prior art related to the estimation of the position of a living body using wireless signals. The results are as described above, but the technologies disclosed in Patent Documents 1 and 2 are device position detection technologies, so there is a problem that the position of the living body cannot be estimated. In addition, in the technology disclosed in Patent Document 3, there is a case where an instantaneous positioning error occurs due to external noise, etc., and there is a problem that it is impossible to know how reliable the positioning result is.

[0055] The inventors have repeatedly conducted in-depth studies on the above problems and have found that the reliability of the estimated results of the position of a biological body in the target space can be evaluated using the wireless signal received by the receiver, thereby completing the present disclosure. That is, the inventors have found that the reliability of the measurement result can be evaluated by calculating a complex transfer function based on the wireless signal received by the receiver and using an evaluation function calculated from a correlation matrix obtained from the calculated complex transfer function or an eigenvalue of the correlation matrix.

[0056] More specifically, in order to achieve the above-mentioned purpose, an estimation method of a technical solution of the present disclosure is an estimation method of an estimation device, wherein the estimation device uses a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements to estimate the position or direction of a biological body existing in an object space, wherein N is a natural number greater than 2, and the estimation method includes: a transfer function calculation step, in which a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements are calculated based on received signals received by each of the N receiving antenna elements during a specified period; and an extraction step, in which a biological component caused by the influence of the biological body is extracted from the plurality of complex transfer functions calculated in the transfer function calculation step. A biological component in each of the N receiving antenna elements; a correlation matrix calculation step, calculating a correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extraction step; a calculation step, calculating one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation step; a reliability estimation step, estimating the reliability of an estimation result when estimating the position or direction of the biological body using biological quantity information representing the value of the number of biological bodies existing in the object space and the one or more eigenvalues ​​calculated in the calculation step; and an estimation step, estimating the position or direction of the biological body by a prescribed method based on the correlation matrix according to the reliability of the estimation result.

[0057] Thus, not only can the position of a living body existing in the target space be estimated using wireless signals, but also the reliability of the estimation result of the position of a living body existing in the target space can be evaluated using wireless signals.

[0058] Here, for example, in the reliability estimation step, (1) ratio information is calculated, wherein the ratio information represents the ratio or difference of adjacent eigenvalues ​​when the one or more eigenvalues ​​calculated in the calculation step are sorted in descending order of value, and (2) when the value represented by the organism quantity information is L, if the ratio information corresponding to the Lth eigenvalue in the sorting is greater than a prescribed value, it is determined that the reliability of the estimation result is high, wherein L is a natural number greater than 1.

[0059] Therefore, the reliability of the estimation result can be evaluated using the eigenvalues ​​of the correlation matrix.

[0060] In addition, in order to achieve the above-mentioned purpose, an estimation method of a technical solution of the present disclosure is an estimation method of an estimation device, wherein the estimation device uses a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements to estimate the direction or position of a biological body existing in an object space, wherein N is a natural number greater than 2, and the estimation method includes: a transfer function calculation step, based on the received signals received by each of the N receiving antenna elements during a specified period, calculating a plurality of complex transfer functions representing the propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements; an extraction step, from the received signals in the transfer function calculation step, calculating a plurality of complex transfer functions representing the propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements; and extracting the complex transfer functions obtained by ... A biological component in each of the N receiving antenna elements as a biological component caused by the influence of the biological body is extracted from the multiple complex transfer functions calculated in the extraction step; a correlation matrix calculation step, calculating a correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extraction step; an estimation step, using the correlation matrix to calculate an evaluation function, and using the calculated evaluation function to estimate the direction or position of the biological body; and a reliability estimation step, using the evaluation function calculated in the estimation step to estimate the reliability of the estimation result when estimating the position or direction of the biological body.

[0061] Thus, the reliability of the estimation result can be evaluated using an evaluation function calculated using the correlation matrix.

[0062] Here, for example, the evaluation function calculated in the estimation step is a MUSIC spectrum.

[0063] In addition, for example, the evaluation function calculated in the estimation step is a spectrum of the Capon method.

[0064] Furthermore, for example, the evaluation function calculated in the estimation step is a spectrum of a beamforming method.

[0065] In addition, for example, in the reliability estimation step, when the estimated reliability is lower than a threshold value, it may be determined that the reliability is low, and when the estimated reliability is greater than the threshold value, it may be determined that the reliability is high.

[0066] In addition, for example, in the estimation step, when the reliability is determined to be low in the reliability estimation step, the following estimation result may be output, which is an estimation result of a past time point determined to be high in the reliability estimation step, among the estimation results of the direction or position of the organism in a past period earlier than the time point determined to be low.

[0067] This makes it possible to use an estimation result with high reliability.

[0068] In addition, for example, in the estimation step, when the reliability is determined to be low in the reliability estimation step, the following estimation result may be output, which is an estimation result of the frequency determined to be high in the reliability estimation step, among the estimation results of estimating the direction or position of the biological body in a frequency range other than the frequency determined to be low in reliability.

[0069] This makes it possible to use an estimation result with high reliability.

[0070] In addition, for example, in the estimation step, when the reliability is determined to be low in the reliability estimation step, the following estimation result may be output, which is the estimation result of the past time point and frequency determined to be high in the reliability estimation step, among the estimation results of the direction or position of the biological body in the past specified period and specified frequency range.

[0071] This makes it possible to use an estimation result with high reliability.

[0072] In addition, for example, when the reliability is judged to be low in the reliability estimation step and the estimation result estimated in the estimation step deviates from the immediately previous estimation result by a time or frequency greater than a threshold, the estimation result of the past time point judged to be high among the estimation results of the past period earlier than the time point judged to be low in the reliability is output.

[0073] This makes it possible to use an estimation result with high reliability.

[0074] In addition, for example, it may also include a storage step, storing the estimation results estimated in the past in the estimation step in a storage unit, and in the estimation step, when the reliability is determined to be low in the reliability estimation step, the average value or central value of the past estimation results stored in the storage unit is calculated within the time range of the first interval set to be longer than the reference interval corresponding to the degree of reliability, and output as the current estimation result.

[0075] This makes it possible to use an estimation result with high reliability.

[0076] In addition, for example, it may also include a storage step, storing the estimation results estimated in the past in the estimation step in a storage unit, and when it is determined that the reliability is low in the reliability estimation step, calculating the average value or central value of the past estimation results stored in the storage unit within the frequency range of the second interval set to be longer than the reference interval corresponding to the degree of reliability, and outputting it as the current estimation result.

[0077] This makes it possible to use an estimation result with high reliability.

[0078] In addition, an estimation device of a technical solution of the present disclosure uses a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements to estimate the position or direction of a biological body existing in an object space, wherein N is a natural number greater than or equal to 2, and the estimation device comprises: a transfer function calculation unit, which calculates a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements based on received signals received by each of the N receiving antenna elements during a prescribed period; and an extraction unit, which extracts, from the plurality of complex transfer functions calculated in the transfer function calculation unit, a biological component of each of the N receiving antenna elements caused by the influence of the biological body. a biological component in each of the N receiving antenna elements extracted by the extraction unit; a correlation matrix calculation unit, which calculates a correlation matrix based on the biological component in each of the N receiving antenna elements extracted by the extraction unit; a calculation unit, which calculates one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation unit; a reliability estimation unit, which estimates the reliability of an estimation result when estimating the position or direction of the biological body using biological quantity information representing the value of the number of biological bodies existing in the object space and the one or more eigenvalues ​​calculated in the calculation unit; and an estimation unit, which estimates the position or direction of the biological body according to the correlation matrix by a prescribed method in accordance with the reliability of the estimation result.

[0079] In addition, a recording medium of a technical solution of the present disclosure is a non-temporary computer-readable recording medium, which records a program, wherein the program causes a computer to execute an estimation method of an estimation device, wherein the estimation device uses a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements to estimate the position or direction of a biological body existing in an object space, wherein N is a natural number greater than or equal to 2, and the program causes the computer to execute: a transfer function calculation step, in which a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements are calculated based on received signals received by each of the N receiving antenna elements during a prescribed period; and an extraction step, in which a plurality of complex transfer functions calculated in the transfer function calculation step are extracted as a signal transmitted by the biological body. The invention relates to a biological component in each of the N receiving antenna elements that affects the biological component caused by the biological component; a correlation matrix calculation step, calculating the correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extraction step; a calculation step, calculating one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation step; a reliability estimation step, estimating the reliability of the estimation result when estimating the position or direction of the biological body using biological quantity information representing the value of the number of biological bodies existing in the object space and the one or more eigenvalues ​​calculated in the calculation step; and an estimation step, estimating the position or direction of the biological body according to a prescribed method based on the correlation matrix corresponding to the reliability of the estimation result.

[0080] In addition, the present disclosure can be implemented not only as a device, but also as an integrated circuit having a processing unit of such a device, or as a method that causes the processing unit constituting the device to perform steps, or as a program that causes a computer to execute these steps, or as information, data or signals representing the program. Moreover, these programs, information, data and signals can also be distributed via a recording medium such as a CD-ROM or a communication medium such as the Internet.

[0081] Hereinafter, the embodiments of the present disclosure are described in detail using the accompanying drawings. In addition, the embodiments described below all represent a preferred specific example of the present disclosure. The numerical values, shapes, materials, constituent elements, configuration positions of constituent elements, connection methods, steps, order of steps, etc. shown in the following embodiments are examples and are not intended to limit the present disclosure. In addition, regarding the constituent elements in the following embodiments that are not recorded in the independent claims representing the highest concept of the present disclosure, they are described as arbitrary constituent elements that constitute a more preferred mode. In addition, in this specification and the accompanying drawings, constituent elements having substantially the same functional structure are represented by the same figure marks, thereby omitting repeated descriptions of these constituent elements.

[0082] (Implementation Method 1)

[0083] The following describes, with reference to the accompanying drawings, a situation in which a transmitter and a receiver using a SIMO (Single Input Multiple Output) structure observe the correlation matrix of a received signal and use the eigenvalues ​​of the correlation matrix to estimate the direction of a biological body in an object space using a prescribed method.

[0084] [Structure of Estimation Device 1]

[0085] Figure 1 1 is a block diagram showing an example of the structure of the estimation device 1 in the first embodiment. Figure 1 It is also shown as Figure 1 The estimation device 1 shown is a biological body 50 as a detection target.

[0086] The estimation device 1 in the first embodiment estimates the direction of one or more living bodies 50 existing in the target space using a transmitter 10 having one transmitting antenna element and a receiver 11 having N (N is a natural number greater than or equal to 2) receiving antenna elements.

[0087] like Figure 1 As shown, the estimation device 1 in the first embodiment includes a transmitter 10 , a receiver 11 , a biological information calculation unit 12 , a calculation unit 13 , a storage unit 14 , a reliability estimation unit 15 , and a direction estimation unit 16 .

[0088] [Transmitter 10]

[0089] The transmitter 10 is composed of a transmission antenna unit 101 and a transmission unit 102 .

[0090] The transmission antenna unit 101 has one transmission antenna element.

[0091] The transmitting unit 102 generates a high frequency signal. The transmitting unit 102 transmits the transmission signal as the generated high frequency signal from one transmission antenna element of the transmitting antenna unit 101. In this embodiment, the transmitting unit 102 transmits a 2.45 GHz sine wave, for example, but the present invention is not limited thereto. The transmitting unit 102 may transmit a transmission signal of another frequency or may transmit a transmission signal generated using another modulation method.

[0092] [Receiver 11]

[0093] The receiver 11 is composed of a receiving antenna unit 111 and a receiving unit 112 .

[0094] The receiving antenna unit 111 has N receiving antenna elements. Figure 1As shown, the receiving antenna unit 111 includes #1 to #M R M R (M R is an integer greater than 2) antenna element. In the receiving antenna unit 111, M R Each of the receiving antenna elements receives a reception signal including a signal transmitted from one transmitting antenna element and a signal reflected by the living body 50 when the living body 50 exists.

[0095] The receiving unit 112 uses each of the N receiving antenna elements to observe (ie, receive) the received signal for a predetermined period of time. More specifically, Figure 1 As shown, the receiving unit 112 will be M R The high-frequency signal received by each receiving antenna element in a predetermined period is converted into a low-frequency signal that can be processed. The receiving unit 112 transmits the converted low-frequency signal to the biological information calculation unit 12.

[0096] [Biological information calculation unit 12]

[0097] like Figure 1 As shown, the biological information calculation unit 12 includes a complex transfer function calculation unit 121 , an extraction unit 122 , and a correlation matrix calculation unit 123 , and calculates the biological information based on the signal transmitted from the receiver 11 .

[0098] <Complex Transfer Function Calculation Unit 121>

[0099] The complex transfer function calculation unit 121 calculates a plurality of complex transfer functions representing propagation characteristics between the transmission antenna element and each of the N reception antenna elements based on the reception signals received by each of the N reception antenna elements in a predetermined period. More specifically, the complex transfer function calculation unit 121 calculates a complex transfer function representing the propagation characteristics between one transmission antenna element and M reception antenna elements based on the low frequency signal transmitted from the receiver 11. R The complex transfer function of the propagation characteristics between the receiving antenna elements. Figure 2 The following will be described in more detail.

[0100] Figure 2 It is schematically indicated Figure 1 1 is a conceptual diagram of the propagation of the transmission wave in the estimation device 1. Figure 2 As shown in FIG. 1 , a part of the transmission wave transmitted from the transmission antenna element of the transmission antenna unit 101 is reflected by the living body 50 and reaches the receiving array antenna of the receiving antenna unit 111. Here, the receiving antenna unit 111 is composed of M RThe receiving array antenna composed of receiving antenna elements is a linear array with an element spacing d. In addition, the direction of the organism 50 observed from the front of the receiving antenna unit 111 is set to θ. In addition, the distance between the organism 50 and the receiving antenna unit 111 is large enough, and the reflected wave from the organism that reaches the receiving antenna unit 111 is regarded as a plane wave.

[0101] Furthermore, the complex transfer function calculation unit 121 can calculate the value of the complex transfer function by using M R The complex received signal vector observed by the receiving antenna elements is calculated to represent the relationship between the transmitting antenna elements and M R The complex transfer function vector of the propagation characteristics between the individual receive antenna elements.

[0102] <Extraction Unit 122>

[0103] The extraction unit 122 extracts the biological component in each of the N receiving antenna elements, that is, the biological component caused by the influence of the biological body 50, from the plurality of complex transfer functions calculated by the complex transfer function calculation unit 121. More specifically, the extraction unit 122 records the complex transfer functions calculated by the complex transfer function calculation unit 121 in a time series, and extracts the biological component caused by the influence of the biological body 50 from the changes in the complex transfer functions. Here, as a method for extracting the biological component caused by the influence of the biological body 50, a method of extracting only the component corresponding to the vibration of the biological body after transforming to the frequency domain such as Fourier transform, or a method of calculating the difference between two complex transfer functions at different times, etc. can be used. By executing these methods, the complex transfer function passing through the fixed object other than the biological body 50 is removed, and only the complex transfer function component passing through the biological body 50 remains. In addition, since there are a plurality of receiving antenna elements, the number of biological body 50 passing components of the complex transfer function corresponding to the receiving antenna element is also a plurality. These are collectively defined as a biological component channel vector.

[0104] <Correlation Matrix Calculation Unit 123>

[0105] The correlation matrix calculation unit 123 calculates a correlation matrix based on the biological component in each of the N receiving antenna elements extracted by the extraction unit 122. More specifically, the correlation matrix calculation unit 123 calculates the correlation matrix R of the biological component channel vector i , the biological component channel vector is composed of multiple complex transfer function components passing through the biological body 50 as biological components.

[0106] [Calculation unit 13]

[0107] The calculation unit 13 calculates one or more eigenvalues ​​of the correlation matrix calculated by the correlation matrix calculation unit 123. More specifically, the calculation unit 13 calculates the correlation matrix R calculated by the correlation matrix calculation unit 123. iThe calculation unit 13 calculates the correlation matrix R i The inherent value of is stored in the storage unit 14.

[0108] The calculation unit 13 calculates the correlation matrix R i Eigenvalue decomposition is performed to calculate eigenvalues. Generally speaking, eigenvalues ​​and eigenvectors each represent a propagation path of a radio wave (transmission wave) from the transmission antenna unit 101 to the reception antenna unit 111, that is, a path. Originally, there are various paths such as direct waves and reflections caused by fixed objects such as walls, and each path corresponds to each eigenvalue and eigenvector. However, in this embodiment, the correlation matrix R i Since the correlation matrix calculation unit 123 calculates the eigenvalues ​​based on the biological component in each of the N receiving antenna elements, the eigenvalues ​​calculated by the calculation unit 13 represent the path reflected by the biological body 50 and the path corresponding to the noise.

[0109] [Storage unit 14]

[0110] The storage unit 14 is composed of a HDD (Hard Disk Drive) or a memory, and stores data and the like used in the processing of the estimation device 1 .

[0111] For example, the storage unit 14 stores calculation results of the calculation unit 13 and data used in the estimation process of the reliability estimation unit 15. In addition, the storage unit 14 stores estimation results estimated in the past by the direction estimation unit 16.

[0112] [Reliability estimation unit 15]

[0113] The reliability estimation unit 15 estimates the reliability of the estimation result when estimating the direction of the living body 50 using the living body quantity information indicating the value of the number of living bodies 50 existing in the target space and one or more inherent values ​​calculated by the calculation unit 13. Here, the living body quantity information is information given in advance, indicating information of the living bodies 50 existing in L persons in the measurement target range, i.e., the target space.

[0114] More specifically, the reliability estimation unit 15 calculates ratio information indicating the ratio or difference of adjacent eigenvalues ​​when the plurality of eigenvalues ​​calculated by the calculation unit 13 are sorted in descending order. Then, when the value indicated by the biological quantity information is L (L is a natural number greater than or equal to 1), the reliability estimation unit 15 determines that the reliability of the estimation result is high when the ratio information corresponding to the Lth eigenvalue in the sorting is greater than or equal to a predetermined value.

[0115] That is, the reliability estimation unit 15 evaluates the reliability of the estimation result performed by the direction estimation unit 16 based on the characteristics of the eigenvalue distribution obtained by sorting the plurality of eigenvalues ​​calculated by the calculation unit 13 from large to small values.

[0116] Figure 3 This is a diagram showing an example of eigenvalue distribution in the first embodiment.

[0117] The intrinsic value distribution is a distribution obtained when the plurality of intrinsic values ​​calculated by the calculation unit 13 are sorted from large to small. Figure 3 As shown in , it is also possible to have a table in which a plurality of unique values ​​are sorted and arranged in each row. Figure 3 In the example shown, in the first row of the table of eigenvalue distribution, four eigenvalues, 20, 18, 0.15, and 0.12, are arranged in order. More specifically, Figure 3 In the first row of the table shown, the largest eigenvalue 20 is configured in the first eigenvalue column, the second largest eigenvalue 18 is configured in the second eigenvalue column, the third largest eigenvalue 0.15 is configured in the third eigenvalue column, and the fourth largest eigenvalue 0.12 is configured in the fourth eigenvalue column. In addition, Figure 3 The same is true for the second and third rows of the table shown, and therefore their descriptions are omitted.

[0118] Figure 4 This is a diagram showing an example of ratio information calculated from the eigenvalue distribution in the first embodiment. Figure 4 The reliability of the estimation result determined based on the ratio information is also shown.

[0119] Ratio information is an example of a feature of an eigenvalue distribution, and is obtained by calculating the ratio or difference between adjacent eigenvalues ​​in the eigenvalue distribution. Figure 4 As shown, the ratio information can also be expressed as Figure 3 The table shows the results of the ratio of adjacent eigenvalues ​​in each row of the eigenvalue distribution. More specifically, Figure 4 The first row of the table shown is configured with Figure 3 In the eigenvalue distribution of the first row shown, the ratio of the first eigenvalue divided by the second eigenvalue is 1.11, the ratio of the second eigenvalue divided by the third eigenvalue is 120, and the ratio of the third eigenvalue divided by the fourth eigenvalue is 1.25.

[0120] The reliability of the estimation result is determined by whether the value indicated by the ratio information of the Lth eigenvalue from the largest eigenvalue is greater than a predetermined value when the value indicated by the biological quantity information is L. Figure 4In the example shown, the value represented by the biological quantity information is 2 (two people), so as the ratio information in the second intrinsic value from the largest intrinsic value, it is determined based on whether the ratio when the second intrinsic value is divided by the third intrinsic value is greater than a specified value such as 10. Figure 4 In the first row of the table shown, since the ratio of the second eigenvalue divided by the third eigenvalue is 120 and is larger than the prescribed value, it is determined that the reliability of the estimation result is high. Figure 4 The same is true for the second and third rows of the table shown, and therefore their descriptions are omitted.

[0121] In addition, the eigenvalue distribution is not limited to being expressed as a table, and may be expressed, for example, by a graph in which the vertical axis is the magnitude of the eigenvalue and the horizontal axis is the number indicating the order of the number starting from the largest eigenvalue in the sorting. Similarly, the ratio information of the eigenvalue distribution is not limited to being expressed as a table, and may be expressed, for example, by a graph in which the vertical axis is the ratio of the eigenvalue and the horizontal axis is the number indicating the order of the number starting from the largest eigenvalue in the sorting.

[0122] Figure 5 This is a block diagram showing an example of a detailed configuration of the reliability estimation unit 15 in the first embodiment.

[0123] In this embodiment, if Figure 5 As shown, the reliability estimation unit 15 includes an eigenvalue distribution calculation unit 151 , a feature determination unit 152 , and a reliability determination unit 153 .

[0124] <Eigenvalue Distribution Calculation Unit 151>

[0125] The eigenvalue distribution calculation unit 151 calculates the eigenvalue distribution obtained by sorting the plurality of eigenvalues ​​calculated by the calculation unit 13 from large to small. For example, the eigenvalue distribution calculation unit 151 may calculate Figure 6 The eigenvalue distribution is shown.

[0126] here, Figure 6 This is a diagram showing another example of the eigenvalue distribution in the first embodiment. Figure 6 The eigenvalue distribution shown is obtained by Figure 5 The eigenvalue distribution calculation unit 151 shown in FIG. Figure 6 In , the vertical axis represents the size of the intrinsic value, and the horizontal axis represents the number of the intrinsic value starting from the largest intrinsic value when the largest intrinsic value is set as the first when multiple intrinsic values ​​are sorted from largest to smallest. Figure 6 ] An example of a graph in which M eigenvalues ​​are sorted from largest to smallest is shown in FIG. , and a value corresponding to noise such as thermal noise is further shown by a dotted line.

[0127] In addition, the eigenvalue distribution calculation unit 151 may calculate ratio information of the eigenvalue distribution described later as the eigenvalue distribution.

[0128] <Feature Determination Unit 152>

[0129] The feature determination unit 152 determines the feature of the eigenvalue distribution calculated by the eigenvalue distribution calculation unit 151. More specifically, the feature determination unit 152 calculates the ratio information indicating the ratio or difference between adjacent eigenvalues ​​in the eigenvalue distribution calculated by the eigenvalue distribution calculation unit 151 as the feature of the eigenvalue distribution. Figure 6 The characteristic of the eigenvalue distribution shown in FIG. 1 can also be calculated by the characteristic determination unit 152 Figure 7 The ratio information of the eigenvalue distribution as shown.

[0130] Figure 7 This is a diagram showing another example of ratio information calculated from the eigenvalue distribution in the first embodiment. Figure 7 The graph of the ratio information of the eigenvalue distribution shown is given by Figure 5 The characteristic determination unit 152 shown in FIG. Figure 7 In , the vertical axis represents the magnitude of the eigenvalue ratio, and the horizontal axis represents the number of the eigenvalue ratio when the ratio of the largest eigenvalue to the next largest eigenvalue is set as the first when multiple eigenvalues ​​are sorted from largest to smallest. Figure 7 The ratio information of the eigenvalue distribution shown in FIG. 1 shows the distribution of the ratio of adjacent eigenvalues ​​when the plurality of eigenvalues ​​calculated in the calculation unit 13 are sorted from largest to smallest. Here, the eigenvalue λ corresponding to the number i (i is a natural number) of the eigenvalue ratio is denoted as i As the numerator, the corresponding intrinsic value λ i Adjacent eigenvalues ​​λ i+1 As the denominator for division, that is, through λ i / λ i+1 (i is a natural number) to calculate the ratio.

[0131] Furthermore, the feature determination unit 152 is not limited to determining the ratio information of the eigenvalue distribution as the feature of the eigenvalue distribution calculated by the eigenvalue distribution calculation unit 151 , but may determine the amount of change in the eigenvalue in the eigenvalue distribution.

[0132] <Reliability Determination Unit 153>

[0133] The reliability determination unit 153 determines the reliability of the estimation result using the feature of the eigenvalue distribution determined by the feature determination unit 152. More specifically, when the value represented by the biological quantity information is L, the reliability determination unit 153 determines the reliability of the estimation result based on whether the value indicated by the ratio information in the Lth eigenvalue from the largest eigenvalue is greater than a predetermined value.

[0134] For example, when the value represented by the biological quantity information is L, the feature determination unit 152 calculates the ratio of the changes in the Lth eigenvalues ​​and the L+1th eigenvalues ​​in the eigenvalue distribution as the ratio information in the Lth eigenvalue. Then, the feature determination unit 152 determines whether the reliability of the estimation result is high by determining whether the calculated eigenvalue ratio in the Lth eigenvalue is greater than a predetermined value.

[0135] exist Figure 7 In the example shown, the value represented by the biological quantity information is L, so the feature determination unit 152 determines whether the ratio of the eigenvalues ​​in the Lth eigenvalue surrounded by the solid line circle is greater than or equal to a predetermined value. Figure 7 In the example shown, as can be seen from the fact that the eigenvalue ratio of the Lth eigenvalue surrounded by the solid circle stands out, the feature determination unit 152 determines that the eigenvalue ratio of the Lth eigenvalue is greater than the specified value and determines that the reliability of the estimation result is high.

[0136] In this way, the feature determination unit 152 can use the features of the eigenvalue distribution determined by the feature determination unit 152 to determine whether the Lth and L+1th eigenvalues ​​in the eigenvalue distribution decrease sharply, thereby determining whether the reliability of the estimation result of the direction estimation unit 16 is high.

[0137] This is because, when there are L living bodies 50 in the target space, there are L propagation paths of radio waves passing through the living bodies 50, and there are L eigenvalues ​​that are statistically significant compared to noise such as thermal noise.

[0138] In other words, when there are L biological bodies 50 in the object space, if the number of eigenvalues ​​that are statistically significant compared to noise is less than L, there are biological bodies 50 whose directions cannot be estimated. In addition, when there are L biological bodies 50 in the object space, if the number of eigenvalues ​​that are statistically significant compared to noise is L+1 or more, images (biological bodies 50) that are larger than the number of biological bodies 50 that actually exist can be seen due to the influence of noise, etc. That is, when there are L biological bodies 50 in the object space, there should be only L eigenvalues ​​that are statistically significant, so there should be a difference between the Lth eigenvalue and the L+1th eigenvalue. Therefore, the feature determination unit 152 can evaluate the reliability of the estimation result of the direction estimation unit 16 by evaluating the size of the difference between the Lth eigenvalue and the L+1th eigenvalue.

[0139] Alternatively, the reliability determination unit 153 may directly output the calculated eigenvalue ratio as a determination result of the reliability of the estimation result of the direction estimation unit 16 .

[0140] [Direction estimation unit 16]

[0141] The direction estimation unit 16 estimates the direction of the biological body 50 by a predetermined method based on the correlation matrix according to the reliability estimated by the reliability estimation unit 15. More specifically, the direction estimation unit 16 estimates the direction observed from the receiving antenna unit 111 of the biological body 50 based on the reliability estimated by the reliability estimation unit 15 using the correlation matrix calculated by the biological information calculation unit 12.

[0142] Here, the direction estimation unit 16 may estimate the direction of the living body 50 using a MUSIC (Multiple Signal Classification) method or a beam forming method.

[0143] In addition, the direction estimation unit 16 may change the output when the reliability estimated by the reliability estimation unit 15 is high or low. For example, when the reliability estimated by the reliability estimation unit 15 is low, the direction estimation unit 16 may discard the estimation result of estimating the direction of the organism 50 based on the correlation matrix of the eigenvalues ​​used for estimating the reliability, and output the previous estimation result. More specifically, when the reliability estimation unit 15 determines that the reliability is low, the direction estimation unit 16 may output the estimation result of the past time point determined by the reliability estimation unit 15 as having high reliability among the estimation results of estimating the direction of the organism 50 in the past period earlier than the time point determined as having low reliability.

[0144] Furthermore, instead of discarding the estimation result, the direction estimation unit 16 may output the estimation result together with information such as a sign indicating low reliability.

[0145] In addition, the direction estimation unit 16 may discard the estimation result and output the last estimation result when the reliability estimated by the reliability estimation unit 15 is low and the estimation result of the previous direction estimation unit 16 deviates by more than a predetermined threshold. In other words, the direction estimation unit 16 may output the estimation result of a past time point determined to be high in the estimation results of a past period earlier than the time point determined to be low in reliability when the reliability estimation unit 15 determines that the reliability is low and the estimation result estimated this time deviates by more than a threshold from the estimation result immediately before.

[0146] In addition, the direction estimation unit 16 may also set an interval set according to the reliability estimated by the reliability estimation unit 15, calculate the time average value or the median value of the past estimation results of the interval, and output it as the estimation result. In other words, when the reliability estimation unit 15 determines that the reliability is low, the direction estimation unit 16 may calculate the time average value or the median value of the past estimation results stored in the storage unit 14 within the time range of the first interval set to be longer than the reference interval according to the determined degree of reliability, and output it as the current estimation result.

[0147] In addition, when the direction estimation unit 16 performs direction estimation using a method such as the MUSIC method, the beamforming method, or the Capon method, the spectrum of the evaluation function is obtained. In such a case, the direction estimation unit 16 may use the maximum value of each spectrum to evaluate the reliability of the estimation result, and output it according to the evaluated reliability.

[0148] [Estimation of the Operation of the Device 1]

[0149] Next, the operation of the estimation device 1 configured as above will be described with reference to the drawings.

[0150] Figure 8 This is a flowchart showing an example of the operation of the estimation device 1 in the first embodiment. Fig. 9 FIG. 1 is a flowchart showing another example of the operation of the estimation device 1 in the first embodiment. Figure 8 The same elements are provided with the same reference numerals.

[0151] First, the estimation device 1 observes a received signal for a predetermined period (S1). More specifically, the estimation device 1 observes a received signal including a reflected signal transmitted from one transmission antenna element and reflected by a living body 50 existing in a target space for a predetermined period.

[0152] Next, the estimation device 1 calculates a complex transfer function (S2) based on the received signal observed in step S1. More specifically, the estimation device 1 calculates a plurality of complex transfer functions representing the propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements based on the received signals received by each of the N receiving antenna elements in a predetermined period. The details are as described above, so the description is omitted here. The same applies to the following.

[0153] Next, the estimation device 1 extracts the biological component as the biological component caused by the influence of the biological body 50 from the complex transfer function calculated in step S2 (S3). More specifically, the estimation device 1 extracts the biological component in each of the N receiving antenna elements as the biological component caused by the influence of the biological body 50 from the plurality of complex transfer functions calculated in step S2.

[0154] Next, the estimation device 1 calculates a correlation matrix of the biological components extracted in step S3 (S4). More specifically, the estimation device 1 calculates a correlation matrix based on the biological components in each of the N receiving antenna elements extracted in step S3.

[0155] Next, the estimation device 1 calculates the eigenvalue of the correlation matrix calculated in step S4 (S5). More specifically, the estimation device 1 calculates one or more eigenvalues ​​of the correlation matrix calculated in step S4.

[0156] Next, the estimation device 1 estimates the reliability of the estimation result using the eigenvalue calculated in step S5 (S6). More specifically, the estimation device 1 estimates the reliability of the estimation result when estimating the direction of the organism 50 using the organism quantity information indicating the value of the number of organisms 50 existing in the object space and one or more eigenvalues ​​calculated in step S4.

[0157] Next, the estimation device 1 estimates the direction of the living body 50 based on the correlation matrix calculated in step S4 according to the reliability estimated in step S6 (S7). Here, the estimation device 1 may estimate the direction of the living body 50 using the MUSIC method or the like based on the correlation matrix calculated in step S4.

[0158] In addition, the estimation device 1 may change the output when the reliability estimated in step S6 is high or low. Fig. 9 Provide specific instructions.

[0159] That is, in step S7, first, the estimation device 1 may determine whether the reliability estimated in step S6 is high (S7A). More specifically, the estimation device 1 may determine whether the reliability estimated in step S6 is high or low based on whether the reliability is equal to or greater than a predetermined value.

[0160] In step S7A, when it is determined that the reliability is high (Yes in S7A), the estimation device 1 may estimate the direction of the living body 50 based on the correlation matrix calculated in step S4 and output the estimation result (S7B).

[0161] On the other hand, in step S7A, when it is determined that the reliability is low (No in S7A), the estimation device 1 can skip the current estimation result estimated based on the correlation matrix calculated in step S4, or discard the current estimation result and output the estimation result of the previous estimation (S7C).

[0162] Fig.10 This is a flowchart showing an example of detailed processing of step S6 in the first embodiment.

[0163] In step S6, first, the estimation device 1 calculates the eigenvalue distribution (S61). More specifically, the estimation device 1 calculates the eigenvalue distribution obtained by sorting the plurality of eigenvalues ​​calculated in step S5 from large to small. For example, the estimation device 1 may calculate Figure 6 A graph of the eigenvalue distribution as shown.

[0164] Next, the estimation device 1 determines the characteristics of the eigenvalue distribution calculated in step S61 (S62). More specifically, the estimation device 1 calculates ratio information indicating the ratio or difference of adjacent eigenvalues ​​in the eigenvalue distribution calculated in step S61 as the characteristics of the eigenvalue distribution. Figure 6 The characteristic of the eigenvalue distribution shown in FIG. 1 can also be calculated by the estimation device 1 as follows Figure 7 A graph of the ratio information of the eigenvalue distribution as shown.

[0165] Next, the estimation device 1 estimates the reliability of the estimation result using the characteristics of the eigenvalue distribution determined in step S62 (S63). More specifically, when the value represented by the organism quantity information is L, the estimation device 1 estimates the reliability of the estimation result based on whether the value indicated by the ratio information in the Lth eigenvalue counted from the largest eigenvalue is greater than a predetermined value. That is, the estimation device 1 estimates the reliability of the estimation result by determining whether the Lth and L+1th eigenvalues ​​decrease sharply using the value L of the number of organisms represented by the organism quantity information in the eigenvalue distribution calculated in step S61.

[0166] (Variation Example)

[0167] In the above-mentioned Embodiment 1, as an example, the transmitter 10 transmits a 2.45 GHz sine wave, but the present invention is not limited thereto. The transmitter 10 may also transmit a multi-carrier signal such as an OFDM (Orthogonal Frequency Division Multiplexing) signal as a transmission wave (radio wave). Hereinafter, the case where the transmitter 10 transmits a multi-carrier signal such as an OFDM signal as a transmission wave will be described as a modified example.

[0168] [Structure of Estimation Device 1 of the Present Modification]

[0169] The estimation device 1 of the present variation is different from the estimation device 1 of the first embodiment in that it processes a multi-carrier signal. Figure 1 , the description will focus on the differences from Implementation 1.

[0170] [Transmitter 10]

[0171] As described above, the transmitter 10 according to the present modification example generates a multi-carrier signal such as an OFDM signal, for example, and transmits the signal from one transmission antenna element included in the transmission antenna unit 101 .

[0172] [Receiver 11]

[0173] The receiver 11 of this modification example is composed of a receiving antenna unit 111 and a receiving unit 112, similarly to the first embodiment. The receiver 11 of this modification example is different from that of the first embodiment in that the receiving unit 112 receives a signal.

[0174] The receiving unit 112 observes the multi-carrier signal for a predetermined period of time in the receiving antenna unit 111 having N receiving antenna elements. More specifically, the receiving unit 112 receives the multi-carrier signal from M R The receiving antenna elements 112 convert the multi-carrier signal received in a predetermined period into a low-frequency signal capable of signal processing, and decompose the multi-carrier signal for each sub-carrier. For example, when the multi-carrier signal is composed of s sub-carriers, the receiving unit 112 decomposes it into s low-frequency signals and transmits them to the biological information calculation unit 12.

[0175] [Biological information calculation unit 12]

[0176] The biological information calculation unit 12 calculates s biological information based on the s low-frequency signals transmitted from the receiving unit 112. Specifically, the biological information calculation unit 12 performs calculation processing of s biological information in parallel (s parallel execution), and finally calculates s correlation matrices.

[0177] [Calculation unit 13]

[0178] The calculation unit 13 calculates s sets of eigenvalues ​​based on the s correlation matrices calculated by the biological information calculation unit 12 .

[0179] [Reliability estimation unit 15]

[0180] The reliability estimating unit 15 estimates the reliability of the estimation results of the s groups when the direction estimating unit 16 estimates the direction of the living body 50, using the eigenvalues ​​of the s groups calculated by the calculating unit 13. The reliability estimating unit 15 estimates the reliability of the estimation results of the s groups when estimating the direction of the living body 50, using the living body quantity information indicating the value of the number of living bodies 50 existing in the target space and the eigenvalues ​​of the s groups calculated by the calculating unit 13. Here, the living body quantity information is information given in advance, indicating that L living bodies 50 exist in the target space as the measurement target range.

[0181] More specifically, the reliability estimation unit 15 calculates ratio information of adjacent eigenvalues ​​when the eigenvalues ​​calculated by the calculation unit 13 are sorted from large to small in the s groups. Then, when the value represented by the biological quantity information is L in each of the ratio information of the s groups, the reliability estimation unit 15 determines that the reliability of the estimation result is high when the ratio information corresponding to the eigenvalue of the Lth group in the sorting is greater than the threshold value.

[0182] Note that the method of estimating the reliability of each subcarrier is the same as that in Embodiment 1, and therefore detailed description thereof will be omitted.

[0183] In this way, the reliability estimation unit 15 can determine whether the reliability of the estimation result of the s groups by the direction estimation unit 16 is high, using the characteristics of the eigenvalue distribution obtained from the eigenvalues ​​of each of the s groups calculated by the calculation unit 13 .

[0184] This is because, similar to the first embodiment, when the direction of the living body 50 existing in the target space can be estimated well, the eigenvalue with a statistical significance appears in accordance with the number of the living bodies 50. On the other hand, when the direction of the living body 50 existing in the target space cannot be estimated well due to noise, the eigenvalue with a statistical significance appears more than the number of the living bodies 50. That is, since the positioning errors and eigenvalues ​​around a certain measuring point (path) among the measuring points (paths) in the target space have similar tendencies, it is known whether the direction of the living body 50 existing in the target space can be estimated well using the eigenvalues.

[0185] Fig.11 This is a diagram showing an example of a positioning error distribution when a multi-carrier signal is used in a modified example of Embodiment 1. The horizontal axis represents subcarriers, and the vertical axis represents time.

[0186] Fig.11 The positioning error distribution shown is obtained by using the OFDM signal and performing the position estimation of the living body 50 based on the MUSIC method for all subcarriers at multiple times. Fig.11 The points depicted in black indicate that the error is below a certain threshold.

[0187] Fig. 12A It means Fig.11 The distribution diagram shown shows whether the ratio of the first eigenvalue and the second eigenvalue at the same timing of the positioning error distribution is greater than a threshold value. Fig. 12B It means Fig.11The distribution of the ratio of the second eigenvalue and the third eigenvalue at the same timing of the positioning error distribution shown is a distribution diagram of whether the distribution is above the threshold. In addition, as in the first embodiment, it means that the first eigenvalue is the largest eigenvalue, the second eigenvalue is the second largest eigenvalue, and the third eigenvalue is the third largest eigenvalue. In addition, this timing means that Fig.11 The time direction and subcarrier direction shown are the same.

[0188] exist Fig. 12A In Fig.11 On the same vertical and horizontal axes, points where the ratio of the first eigenvalue to the second eigenvalue is below a certain threshold are drawn in black. Fig. 12A and Fig.11 For comparison, Fig. 12A The area corresponding to the points where the ratio of the first eigenvalue to the second eigenvalue is above the threshold is concentrated. Fig.11 In the area (i.e., the same area), it can be confirmed that the positioning error tends to be below a certain threshold.

[0189] In addition, Fig. 12B In Fig.11 On the same vertical and horizontal axes, points where the ratio of the second eigenvalue to the third eigenvalue is below a certain threshold are drawn in black. Fig. 12B and Fig.11 For comparison, Fig. 12B The area corresponding to the point where the ratio of the second eigenvalue to the third eigenvalue is above the threshold is concentrated. Fig.11 In the area (i.e., the same area), it can be confirmed that the positioning error tends to be above a certain threshold.

[0190] Therefore, according to Figure 11 to Figure 12B It can be seen that the reliability estimation unit 15 can determine whether the reliability of the estimation result of the direction estimation unit 16 is high by using the distribution of the eigenvalue ratio as a feature of the eigenvalue distribution obtained by the eigenvalues ​​of the s groups calculated by the calculation unit 13 .

[0191] [Direction estimation unit 16]

[0192] The direction estimation unit 16 estimates the direction of the living body 50 by a predetermined method based on the correlation matrix according to the reliability estimated by the reliability estimation unit 15. More specifically, the direction estimation unit 16 estimates the direction of the living body 50 based on the s correlation matrices calculated by the living body information calculation unit 12 and the s reliabilities estimated by the reliability estimation unit 15. Here, the direction estimation unit 16 estimates the direction of the living body 50 by using the MUSIC method or the like for the correlation matrix of each subcarrier.

[0193] In this variation, the direction estimation unit 16 combines the estimation result of the direction of the biological body 50 and the reliability estimated by the reliability estimation unit 15 in a predetermined method as a final estimation result. Here, for example, the direction estimation unit 16 may output the estimation result with the highest reliability among the s estimation results as the final estimation result, or may output the average value or the median value of the estimation results with reliability values ​​above a threshold as the final estimation result.

[0194] In addition, the direction estimation unit 16 can also output the following estimation result, which is the estimation result obtained by estimating the direction of the biological body 50 in the past period earlier than the time point determined by the reliability estimation unit 15 as having low reliability, and is an estimation result of a past time point determined by the reliability estimation unit 15 as having high reliability.

[0195] For example, if you use Fig. 12A To explain, the direction estimation unit 16 may output the estimation result of the time point determined to be high in reliability among the estimation results of the time direction other than a certain time point determined to be low in reliability by the reliability estimation unit 15 .

[0196] In addition, the direction estimation unit 16 may output an estimation result of a frequency determined by the reliability estimation unit 15 to be highly reliable, among the estimation results of the direction of the biological body 50 in a frequency range other than the frequency determined by the reliability estimation unit 15 to be of low reliability.

[0197] For example, if you use Fig. 12A To explain, the direction estimation unit 16 may output the estimation result of the subcarrier determined to be highly reliable among the estimation results of the subcarriers other than the one subcarrier determined to be of low reliability by the reliability estimation unit 15 .

[0198] In addition, the direction estimation unit 16 may also combine the above when the reliability estimation unit 15 determines that the reliability is low. That is, the direction estimation unit 16 may also output the following estimation result, which is the estimation result of the past time point and frequency determined by the reliability estimation unit 15 as having high reliability among the estimation results of the direction of the biological body 50 in the past prescribed period and prescribed frequency range.

[0199] For example, if you use Fig. 12A For explanation, it is assumed that the reliability estimation unit 15 determines that the reliability of a subcarrier at a certain time point (first time point) is low. In this case, the direction estimation unit 16 may output the estimation results of the subcarriers other than the certain subcarrier determined by the reliability estimation unit 15 as having high reliability at time points other than the first time point.

[0200] In addition, the direction estimation unit 16 may also output an estimation result of a past time point that is determined to be of high reliability among the estimation results of a past period earlier than the time point that is determined to be of low reliability, when the reliability estimation unit 15 determines that the reliability is low and the estimation result estimated this time deviates from the estimation result immediately before by more than a threshold value.

[0201] For example, use Fig. 12A First, it is assumed that the estimation result of the first time point of the one subcarrier estimated by the direction estimation unit 16 is the estimation result of this estimation, and the estimation result of the second time point immediately before the first time point of the one subcarrier is the estimation result immediately before. Then, it is assumed that the reliability estimation unit 15 determines that the reliability of a certain first time point of one subcarrier is low, and the estimation result of this estimation deviates from the estimation result of the immediately before estimation by a time greater than a threshold.

[0202] In this case, the direction estimation unit 16 may output the estimation result of the one subcarrier at the past time point determined to be highly reliable, among the estimation results of the one subcarrier in the past period earlier than the first time point determined to be highly reliable by the reliability estimation unit 15 .

[0203] In addition, a second interval longer than the reference interval may be set in accordance with the reliability, and the average value or the central value of the frequency direction of the estimation results included in the second interval in the past estimation results may be calculated and output as the estimation result. In other words, when the reliability estimation unit 15 determines that the reliability is low, the direction estimation unit 16 may calculate the average value or the central value of the past estimation results stored in the storage unit 14 within the frequency range of the second interval set longer than the reference interval in accordance with the determined degree of reliability, and output it as the current estimation result.

[0204] In addition, when the reliability estimation unit 15 determines that the reliability is low, the direction estimation unit 16 may calculate the average value or central value of the past estimation results stored in the storage unit 14 within the time range of the first interval set to be longer than the reference interval corresponding to the determined degree of reliability, and output it as the current estimation result.

[0205] In this way, the length of the interval with low reliability is extended compared to the reference, and then the average value or the median value is calculated, thereby improving the estimation accuracy.

[0206] [Effects, etc.]

[0207] According to the estimation device 1 and the estimation method of the first embodiment and the modified example, it is possible to estimate the direction of the living body 50 existing in the target space and evaluate its reliability using wireless signals. More specifically, the living body component caused by the influence of the living body 50 is extracted from the complex transfer function between one transmitting antenna element and each of the N receiving antenna elements, and the correlation matrix and its eigenvalue are calculated based on the extracted living body component. Then, the direction of the living body 50 is estimated on the basis of estimating the reliability of the measurement result by a predetermined method using the calculated eigenvalue, thereby making it possible to estimate the direction of the living body 50 existing in the target space using wireless signals while knowing the reliability of the estimation result.

[0208] In addition, according to the estimation device 1 and the estimation method of embodiment 1 and the modified example, since only the components related to the biological body 50 are extracted from the received signal, the direction of the biological body 50 can be estimated even if the biological body 50 to be detected does not have a special device such as a transmitter.

[0209] Furthermore, by sequentially performing such direction estimation processing, it is also possible to track the number of people in the living bodies 50 and the positions of the living bodies 50. Thus, the positions of the living bodies 50 existing in the target space can be grasped in real time using wireless signals.

[0210] Furthermore, according to the estimation device 1 and the estimation method of the modified example, it is possible to perform not only the measurement in the time direction but also to perform multiple measurements in the subcarrier direction, i.e., the frequency direction, and to perform reliability determination, thereby searching for and outputting the estimation result with high reliability in the frequency direction, and improving the estimation accuracy.

[0211] (Implementation Method 2)

[0212] In the first embodiment, the case of using a transmitter and a receiver of a SIMO structure is used as an example, but the invention is not limited thereto. A transmitter and a receiver of a MIMO (Multiple Input Multiple Output) structure may be used to observe the correlation matrix of the received signal, and the position of the biological body existing in the target space may be estimated by a predetermined method using the eigenvalue of the correlation matrix. This case will be described below as the second embodiment.

[0213] The following description will focus on the differences from the first embodiment.

[0214] [Structure of Estimation Device 1A]

[0215] Fig.13 This is a block diagram showing an example of the configuration of an estimation device 1A in the second embodiment. Fig.13The estimation device 1A shown is different in the configuration of the transmitter 10A from the estimation device 1 of Embodiment 1. With this configuration, the position of the living body 50 existing in the target space can be estimated, and the reliability of the estimation result of the position can be evaluated.

[0216] [Transmitter 10A]

[0217] The transmitter 10A includes a transmission antenna unit 101A and a transmission unit 102 .

[0218] The transmitting antenna unit 101A includes a plurality of transmitting antenna elements. Fig.13 As shown, with #1 to #M T M T (M T is an integer greater than 2) antenna element.

[0219] Then, the transmitting unit 102 uses the M T Each transmitting antenna element transmits the generated signal, namely, the transmission signal.

[0220] [Complex transfer function calculation unit 121]

[0221] The complex transfer function calculation unit 121 in the second embodiment has the same configuration as that in the first embodiment, but the transmission antenna unit 101A has a plurality of transmission antenna elements, so that a part of the processing is different from that in the first embodiment. Specifically, the estimation device 1A in the present embodiment has M T transmit antenna elements, M R Therefore, the complex transfer function H calculated by the complex transfer function calculation unit 121 becomes M R Line M T Matrix of columns.

[0222] The complex transfer function calculation unit 121 in this embodiment first uses (Equation 1) to convert M R Line M T The complex transfer function of the column is transformed into M R ×M T A vector with 1 row and 1 column. Thus, the complex transfer function calculation unit 121 in the present embodiment can perform subsequent processing, that is, calculation of the complex transfer function, in the same method as the first embodiment.

[0223]

Formula 1

[0224]

[0225] [Direction estimation unit 16]

[0226] The direction estimation unit 16 in Embodiment 2 estimates the position of the living body 50 in a predetermined method based on the correlation matrix, corresponding to the reliability estimated by the reliability estimation unit 15. More specifically, the direction estimation unit 16 in Embodiment 2 estimates the position of the living body 50 by estimating a plurality of directions observed from the receiving antenna unit 111 of the living body 50 using the correlation matrix calculated by the living body information calculation unit 12, corresponding to the reliability estimated by the reliability estimation unit 15.

[0227] In addition, the direction estimation unit 16 in the second embodiment may also change the output when the reliability estimated by the reliability estimation unit 15 is high or low, as in the first embodiment. For example, the direction estimation unit 16 in the second embodiment may discard the estimation result of estimating the position of the biological body 50 based on the correlation matrix of the eigenvalues ​​used for the estimation of the reliability when the reliability estimated by the reliability estimation unit 15 is low, and output the previous estimation result. More specifically, the direction estimation unit 16 in the second embodiment may output the estimation result of the past time point determined by the reliability estimation unit 15 as having high reliability among the estimation results of the position of the biological body 50 in the past period earlier than the time point determined as having low reliability when the reliability estimation unit 15 determines that the reliability is low.

[0228] Furthermore, the direction estimation unit 16 in the second embodiment may output the estimation result together with information such as a sign indicating low reliability, instead of discarding the estimation result.

[0229] In addition, the direction estimation unit 16 in Embodiment 2 may also discard the estimation result and output the last estimation result when the reliability estimated by the reliability estimation unit 15 is low and the estimation result of the direction estimation unit 16 last time deviates by more than a predetermined threshold. In other words, the direction estimation unit 16 may also output the estimation result of a past time point determined to be high in the estimation results of a past period earlier than the time point determined to be low in reliability when the reliability estimation unit 15 determines that the reliability is low and the estimation result estimated this time deviates by more than a threshold from the estimation result immediately before.

[0230] In addition, the direction estimation unit 16 in the second embodiment may also set an interval set corresponding to the reliability estimated by the reliability estimation unit 15, calculate the time average value or the median value of the past estimation results of the interval, and output it as the estimation result. In other words, the direction estimation unit 16 may also calculate the time average value or the median value of the past estimation results stored in the storage unit 14 within the time range of the first interval set to be longer than the reference interval corresponding to the determined degree of reliability, and output it as the current estimation result, when the reliability is determined to be low by the reliability estimation unit 15.

[0231] In addition, the direction estimation unit 16 in Embodiment 2 obtains the spectrum of the evaluation function when performing position estimation using methods such as the MUSIC method, the beamforming method, or the Capon method. In such a case, the direction estimation unit 16 may also use the maximum value of each spectrum to evaluate the reliability of the estimation result and output it according to the evaluated reliability.

[0232] In addition, the transmitter 10A may also use a multi-carrier signal such as an OFDM signal as a transmission wave (radio wave) as in the variant of embodiment 1. The structure and effect in this case are the same as in the variant of embodiment 1, except that not only the direction of the organism but also the position of the organism can be estimated as the estimation result, so the description is omitted.

[0233] [Effects, etc.]

[0234] According to the estimation device 1A and the estimation method of the second embodiment, the position of the living body 50 existing in the target space can be estimated using wireless signals. More specifically, the biological component caused by the influence of the living body 50 is extracted from the complex transfer function between the plurality of transmitting antenna elements and the N receiving antenna elements, and the correlation matrix and its eigenvalue are calculated based on the extracted biological component. Then, the position of the living body 50 is estimated and the reliability of the estimation result is evaluated by a predetermined method using the calculated eigenvalue. In this way, the position of the living body existing in the target space can be estimated using wireless signals.

[0235] In addition, the estimation device 1A and the estimation method of embodiment 2 are also the same as the estimation device 1 of embodiment 1. Since only the components related to the biological body 50 are extracted from the received signal, the position of the biological body and the reliability of its measurement results can be estimated even if the biological body 50 to be detected does not have a special device such as a transmitter.

[0236] In the second embodiment, since the transmitter 10A has two or more transmitting antenna elements, it is possible to estimate not only the direction in which the living body 50 exists but also the position.

[0237] Furthermore, by continuing to perform such position estimation for a predetermined time, it is possible to track the positions of one or more living bodies for a predetermined time.

[0238] (Implementation 3)

[0239] In Embodiments 1 and 2, the reliability of the estimated result of the direction or position of the biological body is estimated using the eigenvalue of the observed received signal, but the invention is not limited thereto. The reliability of the estimated result of the direction or position of the biological body may also be estimated using the evaluation function spectrum of the arrival direction estimation method represented by the MUSIC method or the Capon method. This case is described below as Embodiment 3. The following description focuses on the differences from Embodiment 1.

[0240] [Structure of Estimation Device 1B]

[0241] Fig.14 This is a block diagram showing an example of the configuration of an estimation device 1B in the third embodiment. Fig.14 The illustrated estimation device 1B is different from the estimation device 1 according to the first embodiment in the configurations of a direction estimation unit 16B and a reliability estimation unit 15B.

[0242] [Direction Estimation Unit 16B]

[0243] The direction estimation unit 16B calculates an evaluation function using the correlation matrix calculated by the correlation matrix calculation unit 123, and estimates the direction or position of the biological body 50 using the calculated evaluation function. Here, the evaluation function calculated in the direction estimation unit 16B may be a MUSIC spectrum, a spectrum of the Capon method, or a spectrum of the beamforming method.

[0244] In this embodiment, the direction estimation unit 16B uses the eigenvalues ​​and eigenvectors calculated by the calculation unit 13 and stored in the storage unit 14 to perform direction estimation using an arrival direction estimation method such as the MUSIC method, the Capon method, or the beamforming method, as in Embodiment 1. In addition, the direction estimation unit 16B transmits the spectrum of the evaluation function used in the arrival direction estimation method to the reliability estimation unit 15B.

[0245] When any one of the MUSIC method, the Capon method, and the beamforming method is used, the direction or position of the living body 50 can be estimated in the same manner, so the following description will take the case of using the MUSIC method as an example.

[0246] That is, the direction estimation unit 16B calculates the eigenvalue Λ and the eigenvector [Formula 2] based on the eigenvalue Λ and the eigenvector calculated by the calculation unit 13. and the number L of organisms existing in the target space (sensing range) of the estimation device 1B, and the evaluation function P represented by (Formula 2) is calculated. music The spectrum of the angle θ of (θ). This spectrum is called the MUSIC spectrum.

[0247]

Formula 3

[0248]

[0249] The direction estimation unit 16B searches for the upper L maximum values ​​of the MUSIC spectrum represented by (Equation 2) and estimates the corresponding θ as the direction of the biological body. In addition, the direction estimation unit 16B outputs the P corresponding to the upper L maximum values. music The value of (θ).

[0250] [Reliability Estimation Unit 15B]

[0251] The reliability estimation unit 15B uses the evaluation function calculated in the direction estimation unit 16B to estimate the reliability of the estimation result when estimating the position or direction of the biological body 50. The reliability estimation unit 15B determines that the reliability of the estimation result is low when the estimated reliability is lower than the threshold value, and determines that the reliability of the estimation result is high when the estimated reliability is greater than the threshold value.

[0252] In this embodiment, the reliability estimation unit 15B uses the evaluation function P calculated by the direction estimation unit 16B. music The reliability estimation unit 15B can directly use the evaluation function P music The maximum value of (θ) can also be used to calculate the evaluation function P music The ratio of the local maximum value to the local minimum value of (θ) is used. Hereinafter, an example of using the ratio of the local maximum value to the local minimum value will be described.

[0253] That is, the reliability estimation unit 15B calculates the evaluation function P music The L maximum values ​​of (θ) are respectively determined to be above the threshold value. When the reliability estimation unit 15B determines that a part of the L maximum values ​​is above the threshold value, it is estimated that the possibility that θ that obtains the part of the maximum values ​​is the correct direction of the biological body 50 is high. That is, the reliability estimation unit 15B determines that the reliability of the estimation result corresponding to θ that takes the part of the maximum values ​​is high. Here, the threshold value can be a fixed value such as 2dB, or a value determined by the value of L, such as 2dB when L is 2 and 3dB when L is 1.

[0254] Fig.15 FIG. 1 is a diagram showing an example of a graph of an evaluation function in Embodiment 3. Fig.15 In FIG. 5 , as an evaluation function, a graph of the MUSIC spectrum 501 is shown when L is 3. Fig.15 As shown, the MUSIC spectrum 501 has three local maxima 502A, 502B, and 502C.

[0255] The reliability estimation unit 15B uses Fig.15When estimating the reliability of the estimation result of the direction estimation unit 16B, it is determined whether the three local maximum values ​​502A, 502B, and 502C are greater than or equal to the threshold value 503. Fig.15 In the example shown, the reliability estimation unit 15B determines that the local maximum value 502A and the local maximum value 502C are greater than the threshold value, and estimates that the reliability of the estimation result corresponding to the local maximum value 502A and the local maximum value 502C is high. On the other hand, the reliability estimation unit 15B determines that the local maximum value 502B is less than the threshold value, and estimates that the reliability of the estimation result corresponding to the local maximum value 502B is low.

[0256] In addition, the transmitter 10 may use a multi-carrier signal such as an OFDM signal as a transmission wave (radio wave) as in the modification of Embodiment 1. The configuration and effects in this case are the same as in the modification of Embodiment 1, and thus description thereof is omitted.

[0257] In addition, the operation of the estimation device 1B constructed as described above is the same as that described in Implementation Example 1, except that it can estimate not only the direction of the organism but also the position of the organism 50 as the estimation result and that the reliability of the estimation result is evaluated using the evaluation function used when estimating the position, so the description is omitted.

[0258] [Effects, etc.]

[0259] According to the estimation device 1B and the estimation method of the third embodiment, the position of the living body 50 existing in the target space can be estimated using wireless signals. More specifically, the living body component caused by the influence of the living body 50 is extracted from the complex transfer function between one transmitting antenna element and each of the N receiving antenna elements, and the correlation matrix and its eigenvalue are calculated based on the extracted living body component. Then, on the basis of estimating the reliability of the measurement result by a predetermined method using the calculated eigenvalue, by estimating the direction of the living body 50, the position of the living body 50 existing in the target space can be estimated using wireless signals, and the reliability of the estimation result can be obtained.

[0260] Furthermore, according to the estimation device 1B and the estimation method of the third embodiment, since only the component related to the living body 50 is extracted from the received signal, the position of the living body 50 can be estimated even without making the living body 50 to be detected have a special device such as a transmitter.

[0261] In addition, the estimation device 1B and the estimation method of embodiment 3 are also the same as the estimation device 1 of embodiment 1. Since only the components related to the biological body 50 are extracted from the received signal, the position of the biological body and the reliability of its measurement results can be estimated even if the biological body 50 to be detected does not have a special device such as a transmitter.

[0262] Furthermore, by sequentially performing such position estimation processing, it is also possible to track the number of people in the living bodies 50 and the positions of the living bodies 50. Thus, the positions of the living bodies 50 existing in the target space can be grasped in real time using wireless signals.

[0263] Furthermore, according to the estimation device 1B and the estimation method of the third embodiment, it is possible to perform not only the measurement in the time direction but also to perform multiple measurements in the subcarrier direction, i.e., the frequency direction, and to perform reliability determination. Thus, it is possible to search for and output the estimation result with high reliability in the frequency direction, and to improve the estimation accuracy.

[0264] The above describes an estimation device and an estimation method of a technical solution of the present disclosure based on the implementation method, but the present disclosure is not limited to these implementation methods. As long as it does not deviate from the main purpose of the present disclosure, various modifications that can be thought of by those skilled in the art in the art in the present embodiment, or the method of combining the constituent elements in different implementation methods and constructing them are also included in the scope of the present disclosure.

[0265] In addition, the present disclosure can be implemented not only as an estimation device having such characteristic components, but also as an estimation method in which the characteristic components included in the estimation device are set as steps. In addition, it can also be implemented as a computer program that causes a computer to execute the characteristic steps included in such a method. Moreover, such a computer program can of course be circulated via a non-temporary recording medium readable by a computer such as a CD-ROM or a communication network such as the Internet.

[0266] Industrial Applicability

[0267] The present disclosure can be used in an estimation method and an estimation device for estimating the direction or position of a living body using wireless signals, and can be particularly used in an estimation method and an estimation device used in a monitoring device for detecting intrusion of a living body.

Claims

1. An estimation method of an estimation device, wherein the estimation device estimates the position or direction of a biological body existing in a target space using a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements, wherein: N is a natural number greater than 2, and the estimation method includes: a transfer function calculation step of calculating a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements based on reception signals received by each of the N receiving antenna elements in a predetermined period; an extraction step of extracting a biological component in each of the N receiving antenna elements as a biological component caused by the influence of a biological body from the plurality of complex transfer functions calculated in the transfer function calculation step; a correlation matrix calculating step of calculating a correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extracting step; a calculation step of calculating one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation step; a reliability estimating step of selecting, from the one or more eigenvalues ​​calculated in the calculating step, an Lth eigenvalue counted from a maximum eigenvalue when the value represented by the eigenvalue information is L, based on the eigenvalue information representing the value of the number of eigenvalues ​​existing in the object space, and estimating the reliability of an estimation result when estimating the position or direction of the eigenvalue using the selected Lth eigenvalue, wherein L is a natural number greater than or equal to 1; and The estimation step estimates the position or direction of the biological object in a prescribed method according to the correlation matrix corresponding to the reliability of the estimation result.

2. The estimation method according to claim 1, wherein: In the reliability estimation step, (1) calculating ratio information, the ratio information indicating the ratio or difference between adjacent eigenvalues ​​when the one or more eigenvalues ​​calculated in the calculation step are sorted in descending order of value, (2) When the value represented by the biological quantity information is L, if the ratio information corresponding to the L-th eigenvalue in the sorting is equal to or greater than a predetermined value, it is determined that the reliability of the estimation result is high.

3. An estimation method of an estimation device, wherein the estimation device estimates the direction or position of a biological body existing in a target space using a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements, wherein: N is a natural number greater than 2, and the estimation method includes: a transfer function calculation step of calculating a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements based on reception signals received by each of the N receiving antenna elements in a predetermined period; an extraction step of extracting a biological component in each of the N receiving antenna elements as a biological component caused by the influence of a biological body from the plurality of complex transfer functions calculated in the transfer function calculation step; a correlation matrix calculating step of calculating a correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extracting step; an estimating step of calculating an evaluation function using the correlation matrix, and estimating the direction or position of the organism using the calculated evaluation function; and The reliability estimation step estimates the reliability of the estimation result when estimating the position or direction of the biological body using the maximum value of the evaluation function calculated in the estimation step.

4. The estimation method according to claim 3, wherein: The merit function calculated in the estimating step is a MUSIC spectrum.

5. The estimation method according to claim 3, wherein: The merit function calculated in the estimation step is the spectrum of the Capon method.

6. The estimation method according to claim 3, wherein: The merit function calculated in the estimation step is a spectrum of a beamforming method.

7. The estimation method according to any one of claims 3 to 6, wherein: In the reliability estimation step, When the estimated reliability is lower than a threshold value, it is determined that the reliability is low, and when the estimated reliability is equal to or greater than the threshold value, it is determined that the reliability is high.

8. The estimation method according to any one of claims 1 to 6, wherein: In the estimation step, When the reliability is determined to be low in the reliability estimation step, the following estimation result is output, which is an estimation result of a past time point determined to be high in the reliability estimation step, among the estimation results of the direction or position of the organism in a past period earlier than the time point determined to be low.

9. The estimation method according to any one of claims 1 to 6, wherein: In the estimation step, When the reliability is determined to be low in the reliability estimation step, the following estimation result is output, which is the estimation result of the frequency determined to be high in the reliability estimation step, among the estimation results of the direction or position of the biological body in the frequency range other than the frequency determined to be low in reliability.

10. The estimation method according to any one of claims 1 to 6, wherein: In the estimation step, When the reliability is determined to be low in the reliability estimation step, the following estimation result is output, which is the estimation result of the past time point and frequency determined to be high in the reliability estimation step, among the estimation results of the direction or position of the organism in the past specified period and specified frequency range.

11. The estimation method according to any one of claims 1 to 6, wherein: In the estimation step, When the reliability is judged to be low in the reliability estimation step and the estimation result estimated in the estimation step deviates from the immediately previous estimation result by a time or frequency greater than a threshold, the estimation result of the past time point judged to be high among the estimation results of the past period earlier than the time point judged to be low in reliability is output.

12. The estimation method according to any one of claims 1 to 6, wherein: The method further includes a storage step of storing the estimation result estimated in the estimation step in the past in a storage unit. In the estimation step, When the reliability is determined to be low in the reliability estimation step, the average value or central value of the past estimation results stored in the storage unit is calculated within the time range of the first interval set to be longer than the reference interval corresponding to the degree of reliability, and output as the current estimation result.

13. The estimation method according to any one of claims 1 to 6, wherein: The method further includes a storage step of storing the estimation result estimated in the estimation step in the past in a storage unit. When the reliability is determined to be low in the reliability estimation step, the average value or central value of the past estimation results stored in the storage unit is calculated within the frequency range of the second interval set to be longer than the reference interval corresponding to the degree of reliability, and output as the current estimation result.

14. An estimation device for estimating a position or direction of a living body existing in a target space using a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements, wherein: N is a natural number greater than or equal to 2, and the estimation device comprises: a transfer function calculation unit that calculates a plurality of complex transfer functions representing propagation characteristics between the transmission antenna element and each of the N reception antenna elements based on reception signals received by each of the N reception antenna elements in a predetermined period; an extracting unit that extracts a biological component in each of the N receiving antenna elements as a biological component caused by an influence of a biological body from the plurality of complex transfer functions calculated in the transfer function calculating unit; a correlation matrix calculation unit that calculates a correlation matrix based on the biological component in each of the N receiving antenna elements extracted by the extraction unit; a calculation unit that calculates one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation unit; a reliability estimation unit that selects, based on organism quantity information indicating the number of organisms present in the object space, an L-th eigenvalue counted from a maximum eigenvalue when the value indicated by the organism quantity information is L, from the one or more eigenvalues ​​calculated by the calculation unit, and estimates the reliability of an estimation result when estimating the position or direction of the organism using the selected L-th eigenvalue; as well as The estimation unit estimates the position or direction of the biological body according to the correlation matrix in a predetermined method according to the reliability of the estimation result.

15. A non-transitory computer-readable recording medium having a program recorded thereon, the program causing a computer to execute an estimation method of an estimation device, the estimation device estimating a position or direction of a biological body existing in a target space using a transmitter having at least one transmitting antenna element and a receiver having N receiving antenna elements, wherein: N is a natural number greater than or equal to 2, and the program causes the computer to execute: a transfer function calculation step of calculating a plurality of complex transfer functions representing propagation characteristics between the transmitting antenna element and each of the N receiving antenna elements based on reception signals received by each of the N receiving antenna elements in a predetermined period; an extraction step of extracting a biological component in each of the N receiving antenna elements as a biological component caused by the influence of a biological body from the plurality of complex transfer functions calculated in the transfer function calculation step; a correlation matrix calculating step of calculating a correlation matrix based on the biological component in each of the N receiving antenna elements extracted in the extracting step; a calculation step of calculating one or more eigenvalues ​​of the correlation matrix calculated in the correlation matrix calculation step; A reliability estimation step of selecting, based on organism quantity information indicating the number of organisms existing in the object space, an L-th eigenvalue counted from a maximum eigenvalue when the value indicated by the organism quantity information is L from the one or more eigenvalues ​​calculated in the calculation step, and estimating the reliability of an estimation result when estimating the position or direction of the organism using the selected L-th eigenvalue; as well as The estimation step estimates the position or direction of the biological object in a prescribed method according to the correlation matrix corresponding to the reliability of the estimation result.

Images