JP2019197039A - Estimation device, living body number estimation device, estimation method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an estimation device and the like capable of accurately estimating at least one of the number of living bodies and the position and number of living bodies by using a wireless signal. SOLUTION: A biometric information extraction unit 30 that extracts biometric information that is a component corresponding to one or more living bodies existing in a space, and an eigenvector calculation unit 70 that calculates one or more eigenvectors of a biometric correlation matrix obtained from the biometric information. And a first position estimation unit 40 that estimates a position including a virtual image in one or more living bodies by a predetermined position estimation method using the biometric correlation matrix, and a plurality of first steering vectors stored in the storage unit 50. Of these, a second steering vector output unit 60 that extracts and outputs a first steering vector corresponding to each position including one or more virtual images of the living body estimated by the first position estimation unit 40 as a second steering vector, A second position estimation unit that estimates at least one of the position and the number of one or more living bodies using the eigenvector and the second steering vector. 0 and a. [Selection diagram] Figure 1

Description

  The present disclosure relates to an estimation apparatus, a living body number estimation apparatus, an estimation method, and a program, and in particular, an estimation apparatus and a living body number estimation apparatus that perform estimation of at least one of the position of a living body and the number of living bodies using a radio signal. The present invention relates to an estimation method and a program.

  A technique for detecting a detection target using a signal transmitted wirelessly has been developed (see, for example, Patent Document 1).

  Patent Document 1 discloses a technique that can know the number and positions of persons to be detected by analyzing eigenvalues of components including Doppler shift using a Fourier transform for a signal received wirelessly. ing.

Japanese Patent Laying-Open No. 2015-117972 JP 2014-228291 A Japanese Patent No. 5044702 Japanese Patent No. 5025170

  However, in the technique disclosed in Patent Document 1, in a situation where the difference in the size of the eigenvalue corresponding to a living body is small, such as when the number of living bodies that are detection targets is large, the estimation accuracy of the number of living bodies that are detection targets And the estimation accuracy of the position of the living body also decreases.

  The present disclosure has been made in view of the above-described circumstances. An estimation apparatus, a biological number estimation apparatus, an estimation method, and a program that can accurately estimate at least one of the position and number of a living body using a wireless signal are provided. The purpose is to provide.

  In order to achieve the above object, an estimation device or the like according to an embodiment of the present disclosure includes a component corresponding to one or more living bodies existing in the predetermined space from a reception signal received from a signal transmitted to the predetermined space. A biometric information extraction unit for extracting biometric information, an eigenvector calculation unit for calculating one or more eigenvectors of a biocorrelation matrix obtained from the biometric information, and a predetermined position estimation method using the biocorrelation matrix, The first position estimation unit that estimates a position including a virtual image in the one or more living bodies, and the first position estimation unit that is estimated by the first position estimation unit among a plurality of first steering vectors stored in advance in a storage unit A second steering vector output unit for extracting and outputting a first steering vector corresponding to each of the positions including the virtual image of the living body as a second steering vector; Using a chromatic vector and the second steering vector, the one or more biological positions and a few and a second position estimation unit for estimating at least one.

  According to the estimation device and the like of the present disclosure, it is possible to accurately estimate at least one of the position and the number of living bodies using a radio signal.

FIG. 1 is a block diagram showing the configuration of the sensor in the first embodiment. FIG. 2 is a diagram illustrating an example of an arrangement of transmitters and receivers and a concept of a position estimation method of the estimation device. FIG. 3 is a diagram illustrating an example of a result of calculating a correlation between the second steering vector and the eigenvector and an evaluation function in the first embodiment. FIG. 4 is a flowchart showing the sensor number estimation process in the first embodiment. FIG. 5 is a block diagram showing a configuration of the first position estimating unit in the modification of the first embodiment. FIG. 6 is a block diagram illustrating a configuration of the composite sensor according to the second embodiment. FIG. 7A is a conceptual diagram of an estimated position distribution calculated from the position and the number of persons estimated by one sensor in the second embodiment. FIG. 7B is a conceptual diagram of an estimated position distribution calculated from the position and the number of people estimated by one sensor in the second embodiment. FIG. 8 is a conceptual diagram illustrating an example of the estimated position distribution calculated by the living body number estimation unit according to the second embodiment. FIG. 9 is a flowchart illustrating the number estimation process of the composite sensor according to the second embodiment. FIG. 10 is a diagram illustrating an environment of an experiment using the estimation method according to the second embodiment. FIG. 11A is a diagram showing an estimated position distribution by the Capon method. FIG. 11B is a diagram illustrating an estimated position distribution in the embodiment. FIG. 12 is a diagram showing a spectrum indicating the variance of the estimated position distribution shown in FIG. 11B.

(Knowledge that became the basis of this disclosure)
Techniques for detecting a detection target using a signal transmitted wirelessly have been developed (see, for example, Patent Documents 1 to 4).

  For example, Patent Document 1 discloses a technique for estimating the number and positions of persons to be detected by analyzing eigenvalues of components including a Doppler shift using Fourier transform. Specifically, Fourier transformation is performed on the received signal, an autocorrelation matrix is obtained for a waveform from which a specific frequency component is extracted, and the eigenvalue is decomposed to obtain an eigenvalue. In general, each eigenvalue and eigenvector represents a propagation path of a radio wave from a transmitting antenna to a receiving antenna, that is, one path. Originally, there are various paths such as direct waves or reflection by a fixed object such as a wall, and each path corresponds to each eigenvalue and eigenvector. However, in the technique of Patent Document 1, since components that do not include biological information are removed, only paths reflected by the living body and paths corresponding to noise appear in the eigenvalues and eigenvectors. Here, since the value of the eigenvalue corresponding to the noise is smaller than the value of the eigenvalue corresponding to the living body, the number of living bodies can be estimated by counting the number of eigenvalues larger than a predetermined threshold.

  However, in the technique disclosed in Patent Document 1, when the target living body is far away or the number of living bodies is large, the difference between the eigenvalue corresponding to the living body and the eigenvalue corresponding to the noise is reduced, and the accuracy of estimating the number of people is increased. There is a problem of lowering. This is because in a situation where the Doppler effect is very weak, the Doppler shift is affected by internal noise of the receiver, interference waves flying from other than the detection target, and objects that cause Doppler shift other than the detection target. This is because it becomes difficult to detect a weak signal.

  Patent Document 2 discloses a technique for estimating the position of an object using a direction estimation algorithm such as MUSIC (Multiple Signal Classification). Specifically, the receiving station that has received the signal emitted by the transmitting station performs a Fourier transform on the received signal, obtains an autocorrelation matrix for the waveform from which a specific frequency component is extracted, and uses a direction such as the MUSIC method. Apply an estimation algorithm. This makes it possible to estimate the direction of the living body with high accuracy. However, since the MUSIC method used in Patent Document 2 is a method for estimating the direction of the living body position by using the number of living bodies to be detected in advance as a known number, the number of living bodies cannot be estimated.

  Further, for example, in Patent Document 3, the number of incoming waves, that is, the number of transmitters such as mobile phones is calculated from the correlation between eigenvectors of received signals received by a plurality of antennas and steering vectors in a range where radio waves may arrive. An estimation technique is disclosed.

  Also, for example, Patent Document 4 assumes that the number of incoming waves is various for received signals received by a plurality of antennas, calculates an evaluation function using a steering vector for each, and arrives at the maximum evaluation function. A technique for estimating a wave number as a true incoming wave number is disclosed.

  However, the techniques disclosed in Patent Documents 3 to 4 are techniques for estimating the number of transmitters that emit radio waves, and the number of living bodies cannot be estimated.

  In view of the above, the inventors have estimated that at least one of the position and number of living organisms can be accurately estimated using a radio signal without the subject living organism having a special device such as a transmitter. I came up with a device (sensor).

  That is, the estimation apparatus according to an aspect of the present disclosure extracts biological information that is a component corresponding to one or more living bodies existing in the predetermined space from a reception signal that has received a signal transmitted to the predetermined space. A biometric information extraction unit, an eigenvector calculation unit that calculates one or more eigenvectors of a biocorrelation matrix obtained from the biometric information, and a predetermined position estimation method using the biocorrelation matrix, in the one or more living organisms, A first position estimation unit that estimates a position including a virtual image; and a virtual image of the one or more living bodies estimated by the first position estimation unit among a plurality of first steering vectors stored in advance in a storage unit A second steering vector output unit that extracts and outputs a first steering vector corresponding to each position as a second steering vector; the eigenvector; Using a two steering vectors, wherein 1 or more biological positions and a few and a second position estimation unit for estimating at least one.

  With this configuration, it is possible to use two different estimation methods, not an eigenvalue, but an eigenvector and a predetermined position estimation method such as the Capon method. At least one can be accurately estimated. In this specification, a living body whose position has been estimated is referred to as an image, and an image that does not actually exist is defined as a virtual image, and an image that actually exists is defined as a real image.

  Here, for example, the second position estimation unit calculates a product or a correlation between the eigenvector and the second steering vector, and the eigenvector or the first that takes a value equal to or greater than a threshold value among the calculated product or the correlation. The position indicated by the two steering vectors is estimated as the position.

  Thereby, the position of the living body can be accurately estimated using the radio signal.

  In addition, for example, the second position estimation unit estimates the number of the eigenvectors or the second steering vectors that take a value equal to or greater than a threshold value in the calculated product or correlation as the number.

  Thereby, the number of living organisms can be accurately estimated using a radio signal.

  Here, for example, the predetermined position estimation method may be a Capon method, and for example, the predetermined position estimation method may be a Beamformer method.

  As a result, the position estimation method such as the Capon method that can be used even if the number of people is unknown, although the accuracy of the position estimation is poor, by using the method using the eigenvalue in combination with the position and number of the living body using the radio signal. It is possible to accurately estimate at least one of these.

  In addition, for example, the first position estimating unit further includes a prior number estimating unit that estimates the number of persons including virtual images in the one or more living bodies using an eigenvalue of the biological correlation matrix, and the predetermined position estimation The method may be a MUSIC method using the number of persons estimated by the prior number estimation unit.

  Thereby, even if it is an inaccurate number of people including a virtual image in advance, by combining the position estimation method based on the MUSIC method with the number of people estimated using the eigenvalue for the time being and the method using the eigenvalue together, Using a radio signal, it is possible to accurately estimate at least one of the position and the number of living bodies.

  Here, for example, a transmitter having N (N is a natural number of 2 or more) transmission antenna elements, M (M is a natural number of 2 or more) reception antenna elements, and the M reception antenna elements, respectively. And a transfer function calculating unit that calculates a plurality of complex transfer functions representing propagation characteristics between each of the N transmitting antenna elements and the M receiving antenna elements from the received signal received for a predetermined period of time. And the biological information extraction unit is a fluctuation component due to the influence of the living body and is a fluctuation in each of the M receiving antenna elements from the plurality of complex transfer functions calculated by the transfer function calculation unit. A component is extracted as the biological information, and the eigenvector calculating unit extracts the change in each of the M receiving antenna elements extracted by the biological information extracting unit. It calculates the biological correlation matrix from the components, to calculate the eigenvectors calculated the biological correlation matrix.

  Further, the living body number estimation device according to an aspect of the present disclosure may include, for example, a plurality of the estimation devices according to the above aspects and the positions of one or more living organisms that exist in each of the predetermined spaces estimated by the plurality of estimation devices. And an estimated position distribution calculation unit for calculating an estimated position distribution of the entire space including the predetermined space partially overlapping, and one or more existing in each of the predetermined spaces estimated by the plurality of estimation devices And a living body number estimating unit that estimates the living body number that is the number of one or more living bodies existing in the entire space using each of the living body numbers.

  With this configuration, since a plurality of transmitting stations and receiving stations can be linked by using a plurality of estimation devices, the range in which the number of persons can be estimated can be expanded.

  Here, for example, the living body number estimating unit may estimate the number of local variances of positions estimated by the plurality of estimating devices as the living body number. For example, the living body number estimating unit may The number of local maximum values estimated by the plurality of estimation devices may be estimated as the number of living organisms.

  With this configuration, it is possible to count the same living body estimated by a plurality of estimation devices as a single living body, and thus it is possible to widen the range that can be accurately estimated.

  In addition, the estimation method according to an aspect of the present disclosure extracts biological information that is a component corresponding to one or more living organisms existing in the predetermined space from a reception signal received from a signal transmitted to the predetermined space. A biometric information extraction step, an eigenvector calculation step of calculating one or more eigenvectors of a biocorrelation matrix obtained from the biometric information, and a predetermined position estimation method using the biocorrelation matrix, in the one or more living organisms, A first position estimating step for estimating a position including a virtual image; and a virtual image of the one or more living bodies estimated in the first position estimating step among a plurality of first steering vectors stored in advance in a storage unit. The first steering vector corresponding to each position is extracted as a second steering vector and output. When, by using the above and the eigenvectors second steering vector, and a second position estimation step of estimating at least one of the position and number of the one or more biological.

  In addition, a program according to an aspect of the present disclosure extracts a biological information that is a component corresponding to one or more living bodies existing in the predetermined space from a reception signal received from a signal transmitted to the predetermined space. An information extraction step, an eigenvector calculation step for calculating one or more eigenvectors of a biocorrelation matrix obtained from the biometric information, and a virtual image in the one or more living bodies by a predetermined position estimation method using the biocorrelation matrix A position including one or more virtual images of the living body estimated in the first position estimation step among a plurality of first steering vectors stored in the storage unit in advance. The first steering vector corresponding to each is extracted as the second steering vector and output. And flop, with said second steering vector and the eigenvector, and a second position estimation step of estimating at least one of the position and number of the one or more biological, causes the computer to execute.

  Note that the present disclosure is not only realized as an apparatus, but also realized as an integrated circuit including processing means included in such an apparatus, or realized as a method using processing means constituting the apparatus as a step. Can be realized as a program for causing a computer to execute, or as information, data, or a signal indicating the program. These programs, information, data, and signals may be distributed via a recording medium such as a CD-ROM or a communication medium such as the Internet.

  Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Note that each of the embodiments described below shows a preferred specific example of the present disclosure. Numerical values, shapes, materials, components, arrangement positions and connection forms of components, steps, order of steps, and the like shown in the following embodiments are merely examples, and are not intended to limit the present disclosure. In addition, among the constituent elements in the following embodiments, constituent elements that are not described in the independent claims indicating the highest concept of the present disclosure are described as optional constituent elements that constitute a more preferable embodiment. Further, in the present specification and the drawings, the same reference numerals are given to constituent elements having substantially the same functional configuration, and redundant description is omitted.

(Embodiment 1)
Hereinafter, an estimation method of the sensor 100 according to the first embodiment will be described with reference to the drawings.

[Configuration of Sensor 100]
FIG. 1 is a block diagram showing a configuration of sensor 100 in the first embodiment. FIG. 2 is a diagram illustrating an example of the arrangement of the transmitter 10 and the receiver 20 and the concept of the position estimation method of the estimation device 1 according to the first embodiment. A sensor 100 illustrated in FIG. 1 includes an estimation device 1, a transmitter 10, a receiver 20, a biological information extraction unit 30, and a storage unit 50. Hereinafter, the detailed configuration will be described.

[Transmitter 10]
The transmitter 10 has N (N is a natural number of 2 or more) transmission antenna elements. Specifically, the transmitter 10, as shown in FIG. 1, and a transmission unit 11, a transmitting antenna unit 12 having an array antenna of M _T elements. Here, corresponding to the transmit antenna elements of the N M _T element (N is a natural number of 2 or more), M _T is a natural number of 2 or more.

<Transmission antenna unit 12>
Transmitting antenna unit 12 is composed of a transmit array antenna M _T elements. The transmission array antenna M _T element corresponds to N transmit antenna elements, M _T is a natural number of 2 or more.

  The transmission antenna unit 12 transmits a high-frequency signal generated by the transmission unit 11.

<Transmitter 11>
The transmission unit 11 generates a high-frequency signal used to estimate the presence / absence, position, and / or number of people of the living body 200. For example, the transmission unit 11 generates CW (Continuous Wave) and transmits the generated CW from the transmission antenna unit 12 as a transmission wave. The signal to be generated is not limited to a sine wave signal such as CW, but may be a modulated signal (modulated wave signal).

[Receiver 20]
The receiver 20 includes M (M is a natural number of 2 or more) receiving antenna elements and a complex transfer function calculation unit 23. Specifically, as shown in FIG. 1, the receiver 20 includes a reception antenna unit 21, a reception unit 22, and a complex transfer function calculation unit 23.

<Receiving antenna unit 21>
The receiving antenna unit 21 is composed of a reception array antenna having M _R element. Here, the reception array antenna M _R element corresponds to M receive antenna elements, M _R is a natural number of 2 or more. The receiving antenna unit 21 is a receiving array antenna and receives a high frequency signal.

<Receiver 22>
The receiving unit 22 converts the high-frequency signal received by the receiving antenna unit 21 into a low-frequency signal that can be processed using, for example, a down converter. The receiving unit 22 transmits the converted low frequency signal to the complex transfer function calculating unit 23.

  In FIG. 1, the transmitter 10 and the receiver 20 are configured adjacent to each other, but are not limited thereto. For example, as illustrated in FIG. 2, the transmitter 10 and the receiver 20 may be disposed at positions separated from each other.

  In FIG. 1, the transmission array antenna used by the transmitter 10 and the reception array antenna used by the receiver 20 are differently arranged at different positions, but the present invention is not limited to this. The transmitting array antenna and the receiving array antenna used by the transmitter 10 and the receiver 20 may be used in common. Further, the transmitter 10 and the receiver 20 may be shared with hardware of a wireless device such as a Wi-Fi (registered trademark) router or a slave unit.

<Complex transfer function calculator 23>
The complex transfer function calculation unit 23 is an example of a transfer function calculation unit, and each of the N transmission antenna elements and the M reception antenna elements is received from the reception signals received by the M reception antenna elements for a predetermined period. A plurality of complex transfer functions representing the propagation characteristics between and are calculated.

Specifically, the complex transfer function calculation unit 23 calculates a complex characteristic indicating a propagation characteristic between the reception array antenna and the transmission array antenna of the transmission antenna unit 12 from a signal observed by the reception array antenna of the reception antenna unit 21. Calculate the transfer function. In this embodiment, the complex transfer function calculation unit 23, the low frequency signal transmitted by the receiver 22, M _R receive antennas of the receiving antenna unit and the M _T transmit antennas elements of the transmitting antenna section 12 A complex transfer function representing a propagation characteristic between the elements is calculated.

  The complex transfer function calculated by the complex transfer function calculation unit 23 includes a reflected wave and / or a scattered wave that is a signal in which a part of the transmission wave transmitted from the transmission antenna unit 12 is reflected and scattered by the living body 200. May include. The complex transfer function calculated by the complex transfer function calculation unit 23 includes reflected waves that do not pass through the living body 200, such as direct waves from the transmission antenna unit 12 and reflected waves derived from a fixed object. In addition, the amplitude and phase of a signal reflected or scattered by the living body 200, that is, a reflected wave and a scattered wave via the living body 200, constantly vary depending on living body activities such as respiration and heartbeat. In the following description, it is assumed that the complex transfer function calculated by the complex transfer function calculation unit 23 includes a reflected wave and a scattered wave that are signals reflected or scattered by the living body 200.

[Estimation device 1]
The estimation apparatus 1 uses at least one of the position and number of a living body using a radio signal by using a position estimation method such as a Capon method that can be used even when the number of people is unknown and a position estimation method using an eigenvalue. Estimate accurately. In the present embodiment, as illustrated in FIG. 1, the estimation device 1 includes a biological information extraction unit 30, a first position estimation unit 40, a second steering vector output unit 60, an eigenvector calculation unit 70, a second A position estimation unit 80. Note that the estimation device 1 may include a storage unit 50. Moreover, although it is not essential that the estimation apparatus 1 is provided with the transmitter 10 and the receiver 20, you may provide these. Hereinafter, the detailed configuration of the estimation device 1 will be described.

<Biological information extraction unit 30>
The biometric information extraction unit 30 extracts biometric information, which is a component corresponding to one or more living organisms existing in a predetermined space, from a reception signal that has received a signal transmitted to the predetermined space. More specifically, the biological information extraction unit 30 uses a plurality of complex transfer functions calculated by the complex transfer function calculation unit 23 as a fluctuation component due to the influence of the living body, and a fluctuation component in each of the M receiving antenna elements. Are extracted as biological information.

  In the present embodiment, the biological information extraction unit 30 is transmitted from the transmission array antenna of the transmission antenna unit 12 from the signal observed by the reception array antenna of the reception antenna unit 21 and reflected or reflected by one or more living bodies 200. A biological component that is a scattered signal component is extracted.

  More specifically, the biological information extraction unit 30 records the complex transfer function calculated by the complex transfer function calculation unit 23 in a time series that is the order in which the signals are observed. Then, the biological information extraction unit 30 extracts a fluctuation component due to the influence of the living body 200 from changes in the complex transfer function recorded in time series. Hereinafter, the fluctuation component of the complex transfer function due to the influence of the living body 200 is referred to as a biological component or biological information.

  As a method of extracting a biological component, for example, a method of extracting only a biological component after conversion to the frequency domain by Fourier transform or the like, or a method of extracting by calculating a difference between two complex transfer functions at different times. is there. By these methods, the component of the direct wave and the reflected wave that passes through the fixed object is removed, and only the biological component that passes through the living body 200 remains.

In this embodiment, M _T-number transmission antenna elements that constitute the transmission array antenna, receiving antenna elements constituting the receiving array antenna M _{for R} number i.e. multiple, complex transfer corresponding to the transmission and reception array antenna There are a plurality of biological components via the living body 200 of the function. Hereinafter, a matrix in which these biological components are collected is referred to as a biological component channel matrix H (t).

  In this way, the biological information extraction unit 30 calculates the biological component channel matrix H (t) from the time-series complex transfer function. In addition, the biological information extraction unit 30 outputs the biological component channel matrix H (t) obtained from the extracted biological component to the first position estimation unit 40 and the eigenvector calculation unit 70.

<Eigenvector Calculation Unit 70>
The eigenvector calculation unit 70 calculates one or more eigenvectors of the biocorrelation matrix obtained from the biometric information. More specifically, the eigenvector calculation unit 70 calculates a biocorrelation matrix from the biometric information extracted from the biometric information extraction unit 30 and is a variation component in each of the M receiving antenna elements, and calculates the biometric correlation matrix. Calculate the eigenvector of.

In the present embodiment, the eigenvector calculation unit 70 calculates the correlation matrix R from the biological component channel matrix H (t) received from the biological information extraction unit 30 according to (Equation 1). In (Expression 1), E [•] represents an ensemble average, and {•} ^H represents a complex conjugate transpose operation. The correlation matrix R is an example of the biological correlation matrix.

In addition, the eigenvector calculation unit 70 transmits the eigenvector obtained by eigenvalue decomposition of the calculated correlation matrix R to the second position estimation unit 80. Here, when eigenvalue decomposition is performed on the correlation matrix R of the biological component channel matrix H (t), it can be written as the following (Expression 2) to (Expression 4). In (Expression 3), u ₁ to u _N represent eigenvectors having N elements. In (Expression 4), λ ₁ to λ _N represent eigenvalues corresponding to eigenvectors.

  Here, the concept of the calculated eigenvector will be described with reference to FIG. For example, as shown in FIG. 2, the eigenvector calculation unit 70 calculates eigenvectors 1011 -A to 1011 -D from the biological component channel matrix H (t) obtained in the space where the living body 200 -A and the living body 200 -B exist. Suppose that it is calculated. In this case, the calculated eigenvectors 1011 -A and 1011 -C indicate the direction of the living body 200 -B viewed from the reception antenna of the receiver 20 and the transmission antenna of the transmitter 10. The calculated eigenvectors 1011 -B and 1011 -D indicate the direction of the living body 200 -A viewed from the receiving antenna of the receiver 20 and the transmitting antenna of the transmitter 10. This is because each eigenvector calculated by the eigenvector calculation unit 70 represents one of the paths from the transmission antenna to the reception antenna, and thus corresponds to a vector indicating the position of the living body to be detected as viewed from the transmission antenna and the reception antenna. It will be.

<Storage unit 50>
The storage unit 50 stores a plurality of first steering vectors calculated in advance. The storage unit 50 is accessed by a first position estimation unit 40 and a second steering vector output unit 60 which will be described later, and a plurality of stored first steering vectors are referred to.

  Here, a method for calculating a plurality of first steering vectors will be described.

First, according to (Equation 5), a transmission-side first steering vector a _t (θ) representing a phase difference between the antenna elements of the transmission array antenna is calculated at a predetermined interval in a predetermined range. For example, according to (Equation 5), the first steering vector on the transmission side can be calculated in increments of 1 degree from −45 degrees to 45 degrees, where the front direction of the transmission array antenna is 0 degrees.

  In (Equation 5), λ is the wavelength, N is the number of antenna elements constituting the transmission array antenna, j is an imaginary unit, and e is the base of the natural logarithm.

Next, similarly, the reception-side first steering vector a _r (θ) representing the phase difference between the antenna elements of the reception array antenna is set with respect to a predetermined θ in the transmission-side first steering vector a _t (θ). calculate.

  In (Expression 6), λ is a wavelength, M is the number of antenna elements constituting the receiving array antenna, j is an imaginary unit, and e is the base of the natural logarithm.

Then, the transmission-side first steering vector a _t (θ) and the corresponding reception-side first steering vector a _r (θ) are stored in the storage unit 50 as the first steering vector.

<First position estimating unit 40>
The first position estimation unit 40 estimates a position including one or more virtual images in one or more living bodies by a predetermined position estimation method using a biological correlation matrix obtained from biological information. Here, the predetermined position estimation method is the Capon method, but may be a beamformer method.

  In the present embodiment, the first position estimation unit 40 estimates the position candidate of the living body 200 using the biological component channel matrix H (t) obtained from the biological component extracted by the biological information extraction unit 30. The first position estimation unit 40 estimates a position candidate of the living body 200 using a position estimation method that can be used even if the number of living bodies is unknown, such as a beam former method or a Capon method. Note that the beam former method and the Capon method are not as accurate as position estimation methods such as the MUSIC method that are performed with a known number of living bodies, and a virtual image is generated, so that it is impossible to estimate the number of people with high accuracy by itself.

  Below, the case where the candidate of the position of the biological body 200 is estimated from the biological component channel matrix H (t) is demonstrated using the Capon method as an example of the position estimation method which can be used even if the number of living bodies is unknown.

In the Capon method, first, an evaluation function P _Capon corresponding to the power when the directivity main lobe of the receiving array antenna or the transmitting array antenna is directed to a certain direction θ is calculated for every θ (referred to as a Capon spectrum). ) Then, the direction θ in which the Capon spectrum takes a peak is estimated as the target direction.

The following (Equation 7) shows an evaluation function P _Capon used in the Capon method.

More specifically, the position estimation by the Capon method is performed from both the transmission array antenna of the transmission antenna unit 12 and the reception array antenna of the reception antenna unit 21, thereby creating a two-dimensional Capon spectrum and detecting the detected peak. Estimate the position. Note that when detecting a peak, the Capon spectrum may be smoothed to reduce the influence of noise. Hereinafter will be described as an example N _P-number of peaks were detected.

  Here, the concept of the living body position calculated by the Capon method will be described with reference to FIG. FIG. 2 shows a diagram in which the position of the peak of the Capon spectrum estimated by the first position estimation unit 40 by the Capon method is superimposed on the coordinate system in the space where the living body 200-A and the living body 200-B exist. . That is, the peak position 1001, the peak position 1002, and the peak position 1003 indicated by a plus sign represent peak positions estimated by the first position estimation unit 40. Note that the peak position 1001 is a detected peak position (hereinafter also referred to as a peak position) even though no living body actually exists, and is a virtual image.

<Second steering vector output unit 60>
The second steering vector output unit 60 corresponds to each of the positions including one or more virtual images of the living body estimated by the first position estimation unit 40 among the plurality of first steering vectors stored in the storage unit 50 in advance. The first steering vector is extracted and output as a second steering vector.

In the present embodiment, the second steering vector output unit 60 includes N _p peak positions estimated by the first position estimation unit 40 among the first steering vectors calculated in advance and stored in the storage unit 50. The one corresponding to (the position of the living body including the virtual image) is extracted. Specifically, the second steering vector output unit 60 obtains the first steering vector corresponding to the direction θ of the peak position received from the first position estimation unit 40 as viewed from the transmission array antenna (Equation 5) and ( Output N _p pieces according to Equation 6). In the present embodiment, in order to distinguish from the first steering vector stored in the storage unit 50, the first steering vector extracted by the second steering vector output unit 60 is referred to as a second steering vector. This is expressed as a vector a _peak .

<Second position estimating unit 80>
The second position estimation unit 80 estimates at least one of the position and number of one or more living organisms using the calculated eigenvector and the output second steering vector. Specifically, the second position estimation unit 80 calculates a product or correlation between the calculated eigenvector and the output second steering vector, and the eigenvector or first The position indicated by the two steering vectors may be estimated as the position of the living body. In addition, the second position estimation unit may estimate the number of eigenvectors or second steering vectors that take a value equal to or greater than a threshold value among the calculated products or correlations as the number of living bodies. Note that the threshold is 0.5, for example, but may be determined appropriately between greater than 0 and less than 1.

In the present embodiment, the second position estimation unit 80 calculates the correlation between the second steering vector a _peak output from the second steering vector output unit 60 and the eigenvector u calculated by the eigenvector calculation unit 70. Estimate the number and location of living organisms.

Here, for example, the second position estimating unit 80 may estimate the number of living bodies using an evaluation function for calculating a correlation between the second steering vector a _peak and the eigenvector u shown in (Expression 7).

In (Equation 7), by calculating the inner product of the second steering vector _{a peak} and eigenvectors u, calculates the correlation between the second steering vector _{a peak} and eigenvectors u. For this reason, if there is an eigenvector u that has a high correlation with the second steering vector a _peak , the value is close to 1, and if there is no high correlation, the value is close to 0.

  In (Expression 7), an operation for obtaining the maximum correlation value is performed. However, any operation that can determine the presence or absence of a term having a correlation value close to 1 such as a sum can be used.

Hereinafter, an example of calculating the inner product of the second steering vector a _peak and the eigenvector u and calculating these correlations will be described with reference to FIG.

FIG. 3 is a diagram illustrating an example of a result of calculating a correlation and an evaluation function between the second steering vector a _peak and the eigenvector u in the first embodiment.

As shown in FIG. 3, the correlation between the second steering vector a _peak1 and the eigenvector u2 indicated by reference numeral 1101 is 0.9, which is close to 1 beyond the threshold value 0.5. The correlation between the second steering vector a _peak2 and the eigenvector u1 indicated by reference numeral 1102 is 0.83, which is close to 1 exceeding the threshold value 0.5.

Moreover, the evaluation function _{P Peaki} calculation results for _{a PeakNp} from the second steering vector _{a peak 1,} 2 two evaluation functions _{P Peaki} shown by reference numeral 1103 and the code 1104 is greater than the threshold 0.5. Thereby, the estimated number of living bodies is two.

That is, as shown in FIG. 3, with respect to _{a PeakNp} from _{N p} number of second steering vector _{a peak 1} of the second steering vector output section 60 has output the evaluation function _{P Peaki,} calculates. Then, the number of objects larger than the threshold value 0.5 may be counted and output as the estimated number of living bodies. Moreover, you may output the peak position corresponding to the thing exceeding the threshold value 0.5 as a presumed living body position.

3 conceptually corresponding to FIG. 2, the second steering vector a _peak1 corresponding to the peak position 1001 has a value close to 0 because there is no eigenvector having a high correlation. On the other hand, the second steering vector a _peak2 corresponding to the peak position 1002 has a value close to 1 because there are eigenvectors 1011 -A and 1011 -C having high correlation. Further, the second steering vector a _peak3 corresponding to the peak position 1003 also has a value close to 1 because there are eigenvectors 1011 -B and 1011 -D having high correlation. Therefore, the number of objects exceeding the threshold is two, the number of living bodies is estimated to be 2, and the peak positions 1002 and 1003 corresponding to the two objects exceeding the threshold are estimated as the positions of the living bodies.

[Operation of Sensor 100]
A process in which the sensor 100 configured as described above estimates the number of living bodies will be described.

  FIG. 4 is a flowchart showing the biological number estimation process of the sensor 100 according to the first embodiment.

  First, as shown in FIG. 4, the sensor 100 observes a received signal for a predetermined period in the receiver 20 (S10), and calculates a complex transfer function from the observed received signal (S20).

  Next, the sensor 100 records each of the calculated complex transfer functions in time series, and calculates a biological component channel matrix from the recorded time series complex transfer functions (S30). In other words, the sensor 100 extracts biological information, which is a component corresponding to one or more living bodies existing in the predetermined space, from the received signal that has received the signal transmitted to the predetermined space.

  Next, the sensor 100 calculates an eigenvector from the calculated biological component channel matrix (S35). In other words, the sensor 100 calculates one or more eigenvectors of the biological correlation matrix obtained from the biological information.

  Next, the sensor 100 estimates a biological position candidate from the calculated biological component channel matrix using a predetermined position estimation method that can be used even if the number of persons is unknown, such as the Capon method (S40). In other words, the sensor 100 estimates a position including a virtual image in one or more living bodies using the calculated biological correlation matrix by a predetermined position estimation method.

Next, the sensor 100 calculates the N _P-number of the second steering vector corresponding to the estimated biological position candidates (S50). In other words, the sensor 100 determines the first steering vector corresponding to each of the positions including one or more estimated virtual images of the living body among the plurality of first steering vectors stored in advance in the storage unit 50 as the second steering vector. Extracted as and output.

Next, the sensor 100 calculates the correlation between the N _p second steering vectors and the eigenvector (S60), and estimates the number of living bodies or the position of the living body based on the number of correlations calculated in S60 that are equal to or greater than the threshold ( S70). In other words, the sensor 100 estimates at least one of the position and number of one or more living organisms using the eigenvector and the second steering vector.

[Effects]
According to the estimation device 1 and the like of the present embodiment, the number of living bodies existing in a predetermined space can be accurately estimated using a radio signal. Moreover, according to the estimation apparatus 1 etc. of this Embodiment, the position of the corresponding living body can be estimated simultaneously with the estimation of the number of living bodies.

  More specifically, according to the estimation apparatus 1 or the like of the present embodiment, the position estimation method using the eigenvector corresponding to the eigenvalue and the predetermined position estimation method that can be used even when the number of people is unknown, such as the Capon method, are mutually connected. By using two different methods in combination, the accuracy of estimating the number of people can be improved.

  In the method using the eigenvalue, which is an existing method for estimating the number of living bodies, it is difficult to distinguish the eigenvalue corresponding to the living body and the eigenvalue corresponding to the noise with a threshold when the signal-to-noise ratio is small such as when the living body is far away Become. Although the direction of the eigenvector corresponding to the eigenvalue is less susceptible to noise than the eigenvalue, sorting by the corresponding eigenvalue is necessary for use in estimating the number of people, so the number of people cannot be estimated with the eigenvector alone. That is, the eigenvalue is not easily affected by noise, and the accuracy of estimating the number of people decreases when the signal-to-noise ratio is small.

  On the other hand, in a method using a position estimation method that can be used even if the number of people is unknown, such as the Capon method, a virtual image is likely to be generated around the antenna and the like, and thus there is a high possibility that the number of people is erroneously estimated. In other words, in a method using a position estimation method that can be used even if the number of people is unknown, such as the Capon method, the position estimation method is inferior in accuracy of position estimation, and it is estimated that there is a living body at a position where there is no living body (that is, a virtual image is estimated). ), It is impossible to estimate the number of people with high accuracy by itself.

  Therefore, in the present embodiment, the accuracy of the estimation of the number of persons is improved by using both the position estimation method using the eigenvector corresponding to the eigenvalue and the position estimation method such as the Capon method. That is, the estimated position excluding the virtual image can be estimated by associating the calculated eigenvector with the vector (steering vector) associated with the estimated position including the virtual image calculated by the predetermined position estimation method. As described above, the accuracy of the estimated position calculated by the predetermined position estimation method can be improved by using the information by the eigenvector. As a result, it is possible to overcome the problems of both the existing number estimation method using the eigenvalue and the position estimation method that can be used even if the number of people is unknown, such as the Capon method, and exists in a predetermined space. The number and position of living organisms can be estimated with high accuracy.

  In this disclosure, in order to discriminate the virtual image included in the estimated position calculated by the predetermined position estimation method, the correlation between the eigenvector and the steering vector is calculated as an index for quantitatively evaluating which image is probable. And eliminate the virtual image. As a result, an estimation method that is resistant to noise and has a wide detection range can be realized.

(Modification)
In the above embodiment, the position estimation method that can be used even when the number of people is unknown, such as the Capon method, has been described as the predetermined position estimation method. However, the present invention is not limited to this. A position estimation method based on the MUSIC method in which the number of persons is known may be used. Hereinafter, this case will be described as a modified example, focusing on differences from the first embodiment.

  FIG. 5 is a block diagram showing a configuration of first position estimating unit 40A in the modification of the first embodiment. As illustrated in FIG. 5, the first position estimation unit 40 </ b> A includes a prior number of people estimation unit 401 and a candidate position estimation unit 402.

  The prior number estimation unit 401 estimates the number of persons including virtual images in one or more living bodies using the eigenvalues of the biological correlation matrix. In this modification, the prior person estimation unit 401 uses eigenvalues obtained by the eigenvector calculation unit 70 eigenvalue decomposition of the correlation matrix R before the candidate position estimation unit 402 estimates the position of the living body to be detected. Thus, the number of living bodies to be detected is estimated. Here, the number of people estimated by the prior number of people estimation unit 401 is a number of people estimated for the time being using eigenvalues, and is an inaccurate number of people including a virtual image.

  The candidate position estimation unit 402 estimates a candidate position including a virtual image in one or more living bodies by using the MUSIC method using the number of persons estimated by the prior number estimation unit 401 as a predetermined position estimation method. In this modification, the candidate position estimation unit 402 estimates the position candidate of the living body 200 using the MUSIC method using the inaccurate number of people estimated by the prior number estimation unit 401 as known.

  As a result, the estimation apparatus according to the present modified example uses not only the position estimation method that can be used even if the number of people is unknown, such as the Capon method, but also the position estimation method based on the MUSIC method in which the number of people is known. One can be accurately estimated.

(Embodiment 2)
In the first embodiment, a case where at least one of the position and the number of living bodies is estimated using a single unit, that is, one sensor 100 has been described. In the second embodiment, a case where at least one of the position and the number of living bodies is estimated using two or more sensors 100 will be described.

  Hereinafter, a sensor including two or more sensors 100 is referred to as a composite sensor 101.

[Configuration of Composite Sensor 101]
FIG. 6 is a block diagram illustrating a configuration of the composite sensor 101 according to the second embodiment. Elements similar to those in FIG. 1 are denoted by the same reference numerals, and detailed description thereof is omitted.

  The composite sensor 101 is an example of a living body number estimation apparatus, and includes a plurality of sensors 100 described in the first embodiment. The composite sensor 101 uses the estimation results of the plurality of sensors 100 to estimate the position and number of living bodies in a wider range and with higher accuracy. In the example illustrated in FIG. 6, the composite sensor 101 includes sensors 100-A to 100-D, each of which is a sensor 100, an estimated position distribution calculation unit 120, and a living body number estimation unit 130. In FIG. 6, the composite sensor 101 includes four sensors 100 to stretch around a room (that is, a predetermined space) where a plurality of living bodies 200 (living bodies 200-A and 200-B) exist. Not limited to this. The number of sensors 100 included in the composite sensor 101 is not limited as long as it is two or more. Hereinafter, the detailed configuration will be described.

[Sensor 100-A to Sensor 100-D]
Each of the sensors 100-A to 100-D is the sensor 100 described in the first embodiment, and is between the transmitter 10-A and the receiver 20-A to the transmitter 10-D and the receiver 20-D. The position and number of living organisms are estimated using wireless signals transmitted and received between them. In the present embodiment, each of the sensors 100-A to 100-D estimates the number of living bodies 200 and the position of the living body 200. Then, each of the sensors 100-A to 100-D outputs the estimated number of the living body 200 and the position of the living body 200 to the estimated position distribution calculating unit 120.

[Estimated position distribution calculation unit 120]
The estimated position distribution calculation unit 120 is based on the positions of one or more living bodies 200 existing in each of the predetermined spaces, which are estimated by the plurality of sensors 100, and the estimated position distribution of the entire space including a part of the predetermined space. Is calculated.

  In the present embodiment, the estimated position distribution calculation unit 120 includes the number of living bodies 200 estimated by each of the sensors 100-A to 100-D (referred to as estimated number of persons) and the positions of the living bodies 200 (referred to as estimated positions). Receive. The estimated position distribution calculation unit 120 calculates the estimated position distribution by plotting the received estimated number of persons and estimated position on a plane or space.

  More specifically, the estimated position distribution calculation unit 120 first determines a space (referred to as a whole space) that is a measurement range based on a predetermined space that is measured by each of the sensors 100-A to 100-D. Specifies the coordinate system. Here, the predetermined spaces that are measured by each of the sensors 100-A to 100-D partially overlap to constitute the entire space. Then, the estimated position distribution calculation unit 120 converts and plots the estimated positions output from each of the sensors 100-A to 100-D into the coordinate system of the entire space as the measurement range. Note that the estimated position distribution calculation unit 120 superimposes time-series data indicating the estimated positions estimated from the sensors 100-A to 100-D and estimated for a period of several seconds during which the living body 200 does not move. You may plot it.

  Hereinafter, the concept of the estimated position distribution calculated by the estimated position distribution calculation unit 120 will be described with reference to the drawings. 7A and 7B are conceptual diagrams of estimated position distributions calculated from the positions and the number of persons estimated by one sensor 100 in the second embodiment. FIG. 8 is a conceptual diagram illustrating an example of the estimated position distribution calculated by the living body number estimation unit 130 in the second embodiment.

  As shown in FIG. 7A, the estimated position distribution calculation unit 120 calculates the estimated positions of the living body 200-A and the living body 200-C in a predetermined space 1201-A that is a measurement target range, for example, output from the sensor 100-A. , Converted into the coordinate system of the entire space 1201 of the measurement range and plotted. In the example shown in FIG. 7A, the predetermined space 1201-A constitutes a partial region (left region) of the entire space 1201. Similarly, as illustrated in FIG. 7B, the estimated position distribution calculation unit 120 outputs, for example, the living body 200-A and the living body 200-B in a predetermined space 1201-B, which is a measurement target range, output from the sensor 100-B. The estimated position is converted into the coordinate system of the entire space 1201 of the measurement range and plotted. In the example shown in FIG. 7B, the predetermined space 1201-B constitutes a partial region (lower region) of the entire space 1201.

  The estimated position distribution calculation unit 120 then estimates the positions of the living bodies 200-A and 200-C in the predetermined space 1201-A shown in FIG. 7A and the living bodies 200-A and 200 in the predetermined space 1201-B shown in FIG. 7B. The estimated position of −B is superimposed and plotted as shown in FIG. In the example illustrated in FIG. 8, the predetermined space 1201 -A and the predetermined space 1201 -B partially overlap to constitute the entire space 1201.

  In addition, like the living body 200-A shown in FIG. 8, the living body number estimation unit 130 mathematically removes the overlapping of the living body that appears in duplicate from the output from the sensor 100-A and the output from the sensor 100-B. .

[Biological number estimation unit 130]
The living body number estimation unit 130 estimates the number of living bodies, which is the number of one or more living bodies existing in the entire space, using the number of one or more living bodies existing in each of the predetermined spaces estimated by the plurality of sensors 100. To do. Here, the living body number estimation unit 130 may estimate the number of local minimum values of the positions estimated by the plurality of sensors 100 as the living body number. In addition, the living body number estimation unit 130 may estimate the number of local density maximum positions estimated by the plurality of sensors 100 as the living body number.

  In the present embodiment, the living body number estimating unit 130 estimates the number of people using the estimated position distribution calculated by the estimated position distribution calculating unit 120. More specifically, the living body number estimation unit 130 searches for a point where the density of the estimated position distribution has a maximum value, and estimates the number as the number of living bodies 200. In addition, the living body number estimation unit 130 may estimate the point having the searched maximum value as the position of the living body 200. In this manner, the living body number estimation unit 130 mathematically removes the number of living bodies 200 plotted to overlap the estimated position distribution calculated by the estimated position distribution calculation unit 120, and then the living body existing in the measurement range. A number of 200 can be estimated.

  As a method of calculating the density of the estimated position distribution, a method of dividing the space of the measurement range into grids of an appropriate size and counting the number of estimated positions plotted on each grid may be used. It is also possible to use a reciprocal of the variance of a predetermined number of plots close to. However, it is preferable to select a maximum value of the density of the estimated position distribution whose value is equal to or greater than a predetermined threshold value. Thereby, when there is no living body 200 in the measurement range, that is, when there is no person, it is possible to prevent erroneous estimation.

  In addition, the living body number estimation unit 130 may search for a point where the variance of the estimated position distribution takes a minimum value, and estimate the number as the number of living bodies 200.

[Operation of Compound Sensor 101]
Processing in which the composite sensor 101 configured as described above estimates the number of living bodies will be described.

  FIG. 9 is a flowchart showing the biological number estimation process of the composite sensor 101 according to the second embodiment.

  First, as shown in FIG. 9, the composite sensor 101 causes each sensor 100 (sensor 100-A to sensor 100-D) to estimate the position and number of living bodies for a predetermined period (S110).

  Next, the composite sensor 101 calculates an estimated position distribution using the estimation result of each sensor 100 (S120). More specifically, the composite sensor 101 calculates the estimated position distribution by plotting the estimated number of people and positions output by each sensor 100 over the entire space as the measurement range.

  Next, the composite sensor 101 searches for the maximum value of the density of the estimated position distribution calculated in S120 (S130). Note that the composite sensor 101 may search for the minimum value of the variance of the estimated position distribution calculated in S120.

  Finally, the composite sensor 101 estimates the number or position of living bodies by counting the number of local maximum values searched in S130 that have a value equal to or greater than the threshold (S140). More specifically, the composite sensor 101 estimates the number of the maximum values searched in S130 that are equal to or greater than the threshold as the number of living bodies. In addition, the living body number estimation unit 130 estimates the point having the maximum value searched in S130 as the position of the living body. Note that the composite sensor 101 may estimate the number of living bodies or the living body position by counting the number of local minimum values searched in S130.

[Effects]
According to the composite sensor 101 or the like of the second embodiment, the number of living bodies existing in the measurement range can be estimated with high accuracy using a radio signal. More specifically, since the composite sensor 101 according to the second embodiment can link a plurality of sensors 100 in addition to the effects obtained by the sensor 100 according to the first embodiment, it is more widely and highly accurate. In addition, the position and number of living bodies can be estimated. That is, the composite sensor 101 according to the second embodiment estimates the number of living organisms with high accuracy in a measurement range constituted by a plurality of predetermined ranges that is wider than the predetermined space that is the measurement range of each sensor 100. it can. For this reason, the composite sensor 101 according to the second embodiment has a measurement range in which a certain sensor 100 has a metal obstacle, and even if it is difficult for radio waves to reach, the two sensors 100 are linked together. The number of living bodies can be estimated with high accuracy.

(Example)
Here, since evaluation by experiment was performed in order to confirm the effect which concerns on Embodiment 2, it demonstrates as an Example below.

  FIG. 10 is a diagram illustrating an environment in which an experiment is performed using the estimation method according to the second embodiment.

  This experiment was performed in a 6 m wide x 7 m long room where the living body stands in three places (0.5, 2), (2, 2), and (3.5, 2). In this experiment, an element linear array antenna was used for the transmitting and receiving stations indicated as TRx1 to TRx3 in FIG. Note that TRx1 to TRx3 correspond to the transmitting and receiving array antennas constituting the transmitting antenna unit 12 and the receiving antenna unit 21 constituting the sensor 100. In this experiment, the transmission / reception stations TRx1 to TRx3 are arranged at coordinates (0, 0), (2, 0), (4, 0). Further, the interval between the transmitting and receiving array elements constituting the element linear array antenna used in the transmitting and receiving stations TRx1 to TRx3 is 0.5 wavelength, the operating frequency is 2.47125 GHz, the sampling frequency is 100 Hz, and the frequency range of the biological activity to be extracted is 0.3 to 3 .3 Hz.

  FIG. 11A is a diagram illustrating an estimated position distribution according to the Capon method, and FIG. 11B is a diagram illustrating an estimated position distribution in the present embodiment. In FIGS. 11A and 11B, a person's picture is drawn at the position of the real image where the living body actually exists.

  FIG. 11A shows the result of performing the position estimation of the living body using only the Capon method in the room shown in FIG. In FIG. 11A, time-series data indicating estimated positions (+ in the figure) estimated in a period of several seconds in which the living body does not move are plotted and superimposed, but estimated positions estimated only once are plotted. It may be. Further, FIG. 11B shows the superposition of the results of performing biological body position estimation in the embodiment using the Capon method and two different estimation methods of eigenvectors in the room shown in FIG. 10, that is, the estimated position distribution. ing.

  As can be seen by comparing FIG. 11A and FIG. 11B, in FIG. 11B, the estimated positions (+ in the figure) other than the vicinity of the position where the person is drawn are reduced. That is, it can be seen that a large number of virtual images generated by the Capon method can be removed by the method of this embodiment that uses a position estimation method such as the Capon method and a method using eigenvalues in combination.

  FIG. 12 is a diagram showing a spectrum indicating the variance of the estimated position distribution shown in FIG. 11B.

  As shown in FIG. 12, in this experiment, a spectrum indicating the variance of the estimated position distribution shown in FIG. 11B was calculated, and the maximum value of the density of the estimated position distribution was searched. As shown in FIG. 12, since the number of local maximum values (+ in the figure) is three, it was possible to correctly estimate the number of living bodies as three. Thus, it can be seen that the same living body estimated by the plurality of estimation devices overlappingly can be counted as one living body by searching for the maximum value of the density of the estimated position distribution.

  The present disclosure can be used for an estimation device that estimates at least one of the position and number of a living body using a radio signal, and in particular, a measuring device that measures the number or position of a living body, and a control according to the number or position of the living body. It can be used for household electrical appliances that perform or monitoring devices that detect invasion of living bodies.

DESCRIPTION OF SYMBOLS 1 Estimation apparatus 10, 10-A, 10-B, 10-C, 10-D Transmitter 11 Transmission part 12 Transmitting antenna part 20, 20-A, 20-B, 20-C, 20-D Receiver 21 Reception Antenna unit 22 Receiving unit 23 Complex transfer function calculating unit 30 Biological information extracting unit 40, 40A First position estimating unit 50 Storage unit 60 Second steering vector output unit 70 Eigenvector calculating unit 80 Second position estimating unit 100, 100-A, 100-B, 100-C, 100-D Sensor 101 Compound sensor 120 Estimated position distribution calculation unit 130 Number of living body estimation unit 200, 200-A, 200-B, 200-C Living body 401 Preliminary number of people estimation unit 402 Candidate position estimation unit 1001, 1002, 1003 Peak position 1011-A, 1011-B, 1011-C, 1011-D Eigenvector 1201 All During 1201-A, 1201-B predetermined space

Claims (12)

A biological information extraction unit that extracts biological information, which is a component corresponding to one or more living organisms existing in the predetermined space, from a received signal that has received a signal transmitted to the predetermined space;
An eigenvector calculator that calculates one or more eigenvectors of a biocorrelation matrix obtained from the biometric information;
A first position estimating unit that estimates a position including a virtual image in the one or more living bodies by using a predetermined position estimating method using the biological correlation matrix;
Of the plurality of first steering vectors stored in advance in the storage unit, the first steering vector corresponding to each position including the virtual image of the one or more living bodies estimated by the first position estimating unit is used as the second steering vector. A second steering vector output unit for extracting and outputting as a vector;
A second position estimation unit that estimates at least one of the position and number of the one or more living bodies using the eigenvector and the second steering vector;
Estimating device. The second position estimating unit calculates a product or correlation between the eigenvector and the second steering vector, and indicates the eigenvector or the second steering vector that takes a value equal to or greater than a threshold value among the calculated product or the correlation. Estimating the position as said position;
The estimation apparatus according to claim 1. The second position estimation unit estimates the number of the eigenvectors or the second steering vectors taking a value equal to or greater than a threshold value among the calculated products or correlations as the number.
The estimation apparatus according to claim 1 or 2. The predetermined position estimation method is a Capon method.
The estimation apparatus of any one of Claims 1-3. The predetermined position estimation method is a beamformer method.
The estimation apparatus of any one of Claims 1-3. The first position estimating unit further includes a prior number estimating unit that estimates the number of persons including virtual images in the one or more living bodies using eigenvalues of the biological correlation matrix,
The predetermined position estimation method is a MUSIC method using the number of people estimated by the prior number estimation unit.
The estimation apparatus of any one of Claims 1-3. A transmitter having N transmitting antenna elements (N is a natural number of 2 or more);
From the M reception antenna elements (M is a natural number of 2 or more) and the reception signals received for a predetermined period by each of the M reception antenna elements, the N transmission antenna elements and the M reception antenna elements A receiver having a transfer function calculating unit that calculates a plurality of complex transfer functions representing propagation characteristics between each of
The biological information extraction unit uses a plurality of complex transfer functions calculated by the transfer function calculation unit as fluctuation information due to the influence of a living body and fluctuation components in each of the M receiving antenna elements as the biological information. Extract and
The eigenvector calculation unit calculates the biocorrelation matrix from the fluctuation component in each of the M receiving antenna elements extracted by the biometric information extraction unit, and calculates an eigenvector of the calculated biocorrelation matrix.
The estimation apparatus according to any one of claims 1 to 6. Further, a plurality of the estimation devices according to any one of claims 1 to 7,
Estimated position distribution calculation for calculating an estimated position distribution of the entire space consisting of the predetermined space partially overlapping based on the positions of one or more living bodies existing in each of the predetermined spaces, which are estimated by the plurality of estimation devices. And
Biological number estimation for estimating the number of living bodies, which is the number of one or more living bodies existing in the entire space, using the number of one or more living bodies existing in each of the predetermined spaces, estimated by the plurality of estimating devices. Comprising a part,
Biological number estimation device. The living body number estimating unit estimates the number of local minimum values of positions estimated by the plurality of estimating devices as the living body number;
The living body number estimation apparatus according to claim 8. The living body number estimating unit estimates the number of local maximum values of positions estimated by the plurality of estimating devices as the living body number,
The living body number estimation apparatus according to claim 8. A biological information extraction step of extracting biological information, which is a component corresponding to one or more living organisms existing in the predetermined space, from a received signal that has received a signal transmitted to the predetermined space;
An eigenvector calculating step of calculating one or more eigenvectors of a biocorrelation matrix obtained from the biometric information;
A first position estimation step of estimating a position including a virtual image in the one or more living bodies by using a predetermined position estimation method using the biological correlation matrix;
Of the plurality of first steering vectors stored in advance in the storage unit, the first steering vector corresponding to each position including the one or more virtual images of the living body estimated in the first position estimating step is used as the second steering vector. A second steering vector output step for extracting and outputting as a vector;
A second position estimating step of estimating at least one of the position and number of the one or more living bodies using the eigenvector and the second steering vector.
Estimation method. A biological information extraction step of extracting biological information, which is a component corresponding to one or more living organisms existing in the predetermined space, from a received signal that has received a signal transmitted to the predetermined space;
An eigenvector calculating step of calculating one or more eigenvectors of a biocorrelation matrix obtained from the biometric information;
A first position estimation step of estimating a position including a virtual image in the one or more living bodies by using a predetermined position estimation method using the biological correlation matrix;
Of the plurality of first steering vectors stored in advance in the storage unit, the first steering vector corresponding to each position including the one or more virtual images of the living body estimated in the first position estimating step is used as the second steering vector. A second steering vector output step for extracting and outputting as a vector;
A program that causes a computer to execute a second position estimation step of estimating at least one of the position and number of the one or more living bodies using the eigenvector and the second steering vector.