Human body tracking device and human body tracking method

Abstract

The present invention realizes a human body tracking device and a human body tracking method where the accuracy of tracking a human body can be improved. The present invention comprises: a transmission / reception unit (1) that receives reflected waves of transmitted radio waves; and a processing unit (2) that estimates the position of a human body on the basis of an IF signal output from the transmission / reception unit (1). The processing unit (2) comprises: a first detection unit (21) that detects, as first coordinates, the coordinates of a moving object; a second detection unit (22) that detects, as second coordinates, at least the coordinates of a stationary human body; and a tracking processing unit (23) that tracks the human body on the basis of the first coordinates and the second coordinates.

Description

Human body tracking device and human body tracking method

[0001] The present invention relates to a human body tracking device and a human body tracking method.

[0002] BACKGROUND ART Systems that detect a human body using radar (RADAR: Radio Detection And Ranging) and acquire biometric information (hereinafter also referred to as "vital signs") based on body surface displacements of the human body are being introduced.

[0003] In radar-based tracking systems, targets are identified by analyzing reflected waves of radio waves emitted from an antenna. Patent Document 1 discloses a technology for extracting vibration sources, including human body movements such as heartbeats, and beamforming transmitted waves to track the vibration sources. Patent Document 2 discloses a technology for identifying the type of target based on environmental information defined for each location.

[0004] Furthermore, a technique has been disclosed as a vital sign extraction method that utilizes the correlation between the amplitude and phase of a signal obtained from a reflected wave of an FMCW (Frequency Modulated Continuous Wave) radar (for example, Non-Patent Document 1).

[0005] JP 2022-182179 A International Publication No. 2018 / 211948

[0006] H. Choi, H. Song and H. Shin, “Target Range Selection of FMCW Radar for Accurate Vital Information Extraction”, IEEE Access, vol.9, pp. 1261-1270, 2020.

[0007] The technology described in Patent Document 1 detects periodic vibrations caused by external disturbances as vital signs, and may mistakenly identify reflections from stationary objects (hereinafter also referred to as "clutter") as reflections from the human body.

[0008] Furthermore, the technology described in Patent Document 2 requires that environmental information be defined for each position. In other words, an object cannot be identified in a situation where the environmental information is not defined. Furthermore, if the state of the object (variation in displacement) changes, specifically, for example, if a pedestrian stops, the object may be lost.

[0009] The present disclosure has been made in consideration of the above, and aims to provide a human body tracking device and a human body tracking method that can improve the tracking accuracy of human bodies.

[0010] A human body tracking device according to one aspect of the present disclosure is a human body tracking device comprising: a transceiver unit that receives reflected waves of transmitted radio waves; and a processing unit that estimates the position of a human body based on an IF signal output from the transceiver unit, wherein the processing unit comprises: a first detection unit that detects the coordinates of a moving object as first coordinates; a second detection unit that detects the coordinates of at least a stationary human body as second coordinates; and a tracking processing unit that tracks the human body based on the first coordinates and the second coordinates.

[0011] In this configuration, the first coordinates are detected when the human body being detected is moving, and the second coordinates are detected when the human body is stationary, thereby enabling continuous tracking of the human body.

[0012] A human body tracking method according to one aspect of the present disclosure includes a transmission / reception step of receiving reflected waves of transmitted radio waves, and a processing step of estimating the position of the human body based on the IF signal output in the transmission / reception step, wherein the processing step includes a first step of detecting coordinates of a moving object as first coordinates, a second step of detecting coordinates of at least a stationary human body as second coordinates, and a third step of tracking the human body based on the first coordinates and the second coordinates.

[0013] In this configuration, the first coordinates are detected when the human body being detected is moving, and the second coordinates are detected when the human body is stationary, thereby enabling continuous tracking of the human body.

[0014] According to the present disclosure, it is possible to realize a human body tracking device and a human body tracking method that can improve the accuracy of tracking a human body.

[0015] FIG. 1 is a block diagram showing a schematic configuration of a human body tracking device according to an embodiment. FIG. 2 is a conceptual diagram showing the position of an object. FIG. 3 is a diagram showing an example of a specific procedure for a position estimation method in an FMCW radar. FIG. 4 is a conceptual diagram showing the positional relationship between an FMCW radar and a detection target. FIG. 5 is a flowchart showing an example of a human body tracking process in the human body tracking device according to an embodiment. FIG. 6 is a block diagram showing a schematic configuration of a first detection unit. FIG. 7 is a sub-flowchart showing an example of a first coordinate detection process. FIG. 8 is a block diagram showing a schematic configuration of a second detection unit. FIG. 9 is a sub-flowchart showing an example of a second coordinate detection process. FIG. 10 is a conceptual diagram explaining an example of a power distribution calculation procedure. FIG. 11 is a conceptual diagram showing two-dimensional data in which amplitude variances for each distance and angle are arranged. FIG. 12 is a conceptual diagram showing two-dimensional data in which the correlation between amplitude components and phase components for each distance and angle are arranged. FIG. 13 is a conceptual diagram showing two-dimensional data in which the results of element product calculations for each distance and angle are arranged. FIG. 14A is a conceptual diagram showing the position of an object obtained from power distribution. Fig. 14B is a conceptual diagram showing the position of an object obtained by the element product operation result. Fig. 15A is a conceptual diagram showing the time transition of a first coordinate. Fig. 15B is a conceptual diagram showing the time transition of the first coordinate. Fig. 16A is a conceptual diagram showing the time transition of a second coordinate. Fig. 16B is a conceptual diagram showing the time transition of the second coordinate. Fig. 17A is a conceptual diagram showing the time transition of coordinates indicating an estimated human body position. Fig. 17B is a conceptual diagram showing the time transition of coordinates indicating an estimated human body position.

[0016] Hereinafter, a human body tracking device and a human body tracking method according to an embodiment will be described in detail with reference to the accompanying drawings. However, the present disclosure is not limited to the embodiments.

[0017] 1 is a block diagram showing a schematic configuration of a human body tracking device according to an embodiment. In this disclosure, the human body tracking device 100 is a radar (RADAR: Radio Detection And Ranging) that transmits and receives radio waves to track a human body. In this embodiment, the human body tracking device 100 is described as an FMCW (Frequency Modulated Continuous Wave) radar. Since FMCW radar is well known, detailed description thereof may be omitted.

[0018] The human body tracking device 100 according to the embodiment includes a transceiver unit 1 and a processing unit 2.

[0019] The transceiver 1 includes a transmitting antenna Tx(m) (m is a natural number ranging from 1 to M, the number of transmitting antennas) and receiving antennas Rx(n)(n) (n is a number ranging from 1 to N, the number of receiving antennas). FIG. 1 shows an example including two transmitting antennas Tx(1) and Tx(2). FIG. 1 also shows an example including N receiving antennas Rx(1), ..., Rx(N). The present disclosure is not limited by the number of transmitting antennas and receiving antennas.

[0020] The transmitting / receiving unit 1 transmits radio waves, for example, in the millimeter wave band or microwave band, and receives reflected waves Rx of the radio waves. The processing unit 2 estimates the position of the human body based on the IF signal output from the transmitting / receiving unit 1.

[0021] 2 is a conceptual diagram showing the position of an object. In FIG. 2, the origin O of the XY plane is the position of the human body tracking device 100, the X direction is the arrangement direction of the receiving antennas Rx(1), ..., Rx(N), and the Y direction is the direction perpendicular to the arrangement direction of the receiving antennas Rx(1), ..., Rx(N). Coordinate a shown in FIG. 2 indicates the position of the human body, and coordinates b, c, and d indicate the positions of stationary objects. The human body indicated by coordinate a is assumed to be moving in the direction indicated by the dashed arrow.

[0022] 3 is a diagram showing an example of a specific procedure for a position estimation technique in an FMCW radar. Generally, in an FMCW radar, distance FFT (Fast Fourier Transform) processing (hereinafter also referred to as "1D-FFT processing") is performed on an IF signal output from a transmitter / receiver to obtain distance information (step S1), and then velocity FFT processing (hereinafter also referred to as "2D-FFT processing") is performed on the complex signal after the 1D-FFT processing (step S2).

[0023] Then, a peak detection process is performed on the 2D-FFT processing result to obtain velocity information (step S3), and then an angle estimation process is performed based on the distance information and velocity information to obtain angle information of the object (step S4).

[0024] In the position estimation method of the general FMCW radar described above, the detection accuracy decreases when multiple objects are close to each other. Specifically, as shown in Figure 2, when a human body indicated by coordinate a moves and approaches stationary objects indicated by coordinates b, c, and d, the stationary object may be erroneously determined to be a human body. If the stationary object is erroneously determined to be a human body, the coordinates of the stationary object may be mistakenly determined to be the position of the person when tracking the person's coordinates.

[0025] 4 is a conceptual diagram showing the positional relationship between the FMCW radar and the detection target. ｋ denotes the distance in the kth frequency bin (k is an integer from 0 to N-1, and N is the number of samples (number of receiving antennas)), and Δ(t) denotes the distance r ｋ The small fluctuation component of

[0026] The IF signal x(t, n) acquired by the radar transmitter / receiver is expressed by the following equation (1): In the following equation (1), M(t, r) represents the amplitude component at time t and distance r, and represents the distance between the radar 11 and the target for biometric information acquisition, and P(t, r) represents the phase component at time t and distance r.

[0027]

[0028] The amplitude component M(t, r) and the phase component P(t, r) are extracted by performing a Discrete Fourier Transform (DFT) on the above equation (1). The amplitude component M(t, r) is expressed by the following equation (2). The phase component P(t, r) is expressed by the following equation (3). M in the following equation (2) ０ indicates the amplitude of the transmitted signal.

[0029]

[0030]

[0031] Δ(t)<<r ｋ When the amplitude component M(t, r) shown in the above equation (2) is expanded by Taylor expansion, the following equation (4) is obtained.

[0032]

[0033] As shown in the above equations (3) and (4), the amplitude component M(t,r) and phase component P(t,r) of the IF signal x(t,n) shown in the above equation (1) are each proportional to the minute fluctuation component Δ(t). Here, when the detection target shown in FIG. 4 is a human body, the minute fluctuation component Δ(t) includes body surface displacements associated with vital signs such as the human heartbeat. In other words, both the amplitude component M(t,r) and phase component P(t,r) of the IF signal x(t,n) shown in the above equation (1) change depending on the body surface displacement of the human body.

[0034] When the human body is stationary, the small fluctuation component Δ(t) is dominated by the displacement of the body surface. Therefore, when the detection target is a stationary human body, the correlation between the amplitude component M(t, r) and the phase component P(t, r) is high.

[0035] On the other hand, when the detection target is a stationary object other than a human body, the correlation between the amplitude component M(t, r) and the phase component P(t, r) is low. Furthermore, even when the detection target is a human body, when the human body is moving, the body surface displacement of the human body in the minute fluctuation component Δ(t) is relatively lower than when the human body is stationary. By utilizing this property, it is possible to distinguish at least a stationary human body from other objects (stationary objects other than a human body, a moving human body).

[0036] The following describes the detailed configuration and operation of the processing unit 2 in the human body tracking device 100 according to the embodiment. Here, an example of the configuration and operation assuming two-dimensional coordinates is shown.

[0037] 1, the processing unit 2 includes a first detection unit 21, a second detection unit 22, and a tracking processing unit 23. Fig. 5 is a flowchart showing an example of a human body tracking process in the human body tracking device according to the embodiment.

[0038] First, the configuration and operation of the first detection unit 21 will be described. The first detection unit 21 detects the coordinates of a moving object as first coordinates (step S100). FIG. 6 is a block diagram showing a schematic configuration of the first detection unit. The first detection unit 21 includes a 1D-FFT processing unit 211, a 2D-FFT processing unit 212, a peak detection unit 213, an angle estimation unit 214, and a clutter removal unit 215. FIG. 7 is a sub-flowchart showing an example of the first coordinate detection process. In the present disclosure, reflections from stationary objects are also referred to as "clutter."

[0039] The 1D-FFT processing unit 211 performs 1D-FFT processing on the IF signal output from the transmitting / receiving unit 1 to obtain distance information (step S101).

[0040] The 2D-FFT processing unit 212 performs 2D-FFT processing on the complex signal after the 1D-FFT processing.

[0041] The peak detection unit 213 performs peak detection processing on the 2D-FFT processing result to obtain velocity information (step S102).

[0042] The angle estimation unit 214 performs angle estimation processing based on the distance information and velocity information to obtain angle information of the object and also obtains the coordinates of the object (step S103).

[0043] The clutter removal unit 215 determines whether the object coordinates acquired by the angle estimation unit 214 are the coordinates of a stationary object or the coordinates of a moving object, based on the speed information acquired by the peak detection unit 213. Then, the clutter removal unit 215 removes the coordinates of the stationary object from the coordinates of the objects acquired by the angle estimation unit 214 (step S104), outputs the coordinates of the moving object as first coordinates (step S105), and returns to the human body tracking process shown in FIG.

[0044] Next, a description will be given of the configuration and operation of the second detection unit 22. In the present disclosure, the second detection unit 22 detects the coordinates of a stationary human body as second coordinates (step S200).

[0045] Fig. 8 is a block diagram showing a schematic configuration of the second detection unit 22. The second detection unit 22 includes a power distribution calculation unit 221, a storage unit 222, a calculation unit 223, and a peak extraction unit 224. Fig. 9 is a sub-flowchart showing an example of the second coordinate detection process.

[0046] The power distribution calculation unit 221 calculates the power distribution based on the signal X(r, d, n) after 1D-FFT processing (step S201). Fig. 10 is a conceptual diagram illustrating an example of the power distribution calculation procedure. The signal X(r, d, n) after 1D-FFT processing is three-dimensional data arranged in the distance direction (hereinafter also referred to as the "r direction"), the velocity direction (hereinafter also referred to as the "d direction"), and the arrangement direction of the receiving antennas (hereinafter also referred to as the "n direction").

[0047] The power distribution calculation unit 221 averages the signal X(r, d, n) after the 1D-FFT processing in the d direction to generate two-dimensional data X'(r, n) (step S11_1).

[0048] Next, the power distribution calculation unit 221 performs angle estimation processing based on the two-dimensional data X'(r, n) to obtain two-dimensional data X''(r, θn) corresponding to the power distribution (step S11_2). θn indicates an arbitrary angle. This allows amplitude and phase components to be obtained for each distance and each power. The angle estimation processing performed in the power distribution calculation unit 221 may be the same method as the angle estimation processing performed by the angle estimation unit 214 of the first detection unit 21, or may be a different method from the angle estimation processing performed by the angle estimation unit 214 of the first detection unit 21. The present disclosure is not limited to the specific method of the angle estimation processing performed in the power distribution calculation unit 221.

[0049] Then, the power distribution calculation unit 221 generates two-dimensional data M(r, θ) in which amplitude components for each distance and angle are arranged (step S12) and stores it in the storage unit 222 (step S13). As a result, three-dimensional data M(r, θ, t) in which amplitude components for each distance and angle are arranged in the time direction (hereinafter also referred to as the "t direction") is generated. Furthermore, the power distribution calculation unit 221 generates two-dimensional data P(r, θ) in which phase components for each distance and angle are arranged (step S22) and stores it in the storage unit 222 (step S23). As a result, three-dimensional data P(r, θ, t) in which phase components for each distance and angle are arranged in the t direction is generated.

[0050] The calculation unit 223 calculates the amplitude variance for a period T (for example, 1 sec) based on the current time, and generates two-dimensional data σ Ｍ ２ (r, θ, t) is generated (step S202). The amplitude variance is expressed by the following equation (5): Fig. 11 is a conceptual diagram showing two-dimensional data in which amplitude variances for each distance and angle are arranged.

[0051]

[0052] The calculation unit 223 also calculates the degree of correlation between the amplitude component and the phase component over a period T (e.g., 1 sec) based on the current time, and generates two-dimensional data MPC(r, θ, t) in which the degrees of correlation between the amplitude component and the phase component for each distance and angle are arranged (step S203). The degree of correlation is expressed by the following equation (6). Figure 12 is a conceptual diagram showing two-dimensional data in which the degrees of correlation between the amplitude component and the phase component for each distance and angle are arranged.

[0053]

[0054] The calculation unit 223 then performs element product calculation processing on the power distribution indicated by the power intensity for each distance and angle, the amplitude variance for each distance and angle, and the correlation degree for each distance and angle (step S204). Fig. 13 is a conceptual diagram showing two-dimensional data in which the element product calculation results for each distance and angle are arranged.

[0055] The peak extraction unit 224 extracts the element product calculation result (X″×σ Ｍ ２ ×MPC(r, θ, t)) (step S205). An example of the known peak extraction process is the Cell Averaging Constant False Alarm Rate (CA-CFAR) process. A detailed description of the CA-CFAR process will be omitted here.

[0056] Specifically, the peak extraction unit 224 calculates the element product result (X″×σ Ｍ ２ ×MPC(r, θ, t)). Then, the peak extraction unit 224 acquires the coordinate where the element product calculation result is the maximum value, outputs the coordinate as the second coordinate (step S206), and returns to the human body tracking process shown in Fig. 5. Note that the method of acquiring the second coordinate is not limited to this, and for example, it may be a mode in which the coordinate where the element product calculation result is equal to or greater than a predetermined threshold is acquired as the second coordinate, or a mode in which the coordinate where the element product calculation result is the maximum value is acquired as the second coordinate.

[0057] The peak extraction process executed by the peak extraction unit 224 is not limited to the CA-CFAR process. For example, the coordinates at which the element product calculation result is equal to or greater than a predetermined threshold may be output as the second coordinates. As the peak extraction process executed in the human body tracking process in the human body tracking device 100 of the present disclosure, known peak extraction processes including the above-mentioned CA-CFAR process can be applied.

[0058] 14A and 14B are conceptual diagrams showing the position of an object obtained from power distribution and element product calculation results, respectively.

[0059] 2, in Figures 14A and 14B, the origin O of the XY plane is the position of the human body tracking device 100, the X direction is the arrangement direction of the receiving antennas Rx(1), ..., Rx(N), and the Y direction is the direction perpendicular to the arrangement direction of the receiving antennas Rx(1), ..., Rx(N). Coordinates a shown in Figures 14A and 14B indicate the position of the human body, and coordinates b indicate the position of a stationary object.

[0060] As described above, the minute fluctuation component Δ(t) shown in FIG. 2 includes body surface displacements associated with vital signs such as heart rate. In particular, when the human body is stationary, the minute fluctuation component Δ(t) is dominated by the body surface displacements of the human body. This increases the correlation between the amplitude component and the phase component, resulting in a larger element product calculation result in the calculation unit 223. Therefore, as shown in FIG. 14B, coordinate a indicating the position of the human body is extracted as the second coordinate.

[0061] For a stationary object, body surface displacements associated with vital signs such as the heart rate of the human body are not included in the minute fluctuation component Δ(t) shown in Fig. 2. Therefore, the correlation between the amplitude component and the phase component is low, resulting in a small element product calculation result in the calculation unit 223. For this reason, the stationary object indicated by coordinate b in Fig. 14A is excluded from the peak extraction process in the peak extraction unit 224, as shown in Fig. 14B.

[0062] When the human body is moving, the body surface displacement caused by vital signs such as heart rate becomes relatively small compared to the fluctuation component, and therefore may not necessarily be extracted as the second coordinate.

[0063] 5 , the tracking processing unit 23 receives the first coordinates detected by the first detection unit 21 and the second coordinates detected by the second detection unit 22. The tracking processing unit 23 executes a known position estimation process on the first coordinates detected by the first detection unit 21 and the second coordinates detected by the second detection unit 22 (step S300). An example of the known position estimation process is an extended Kalman filter. A detailed description of the extended Kalman filter will be omitted here.

[0064] Specifically, the tracking processing unit 23 applies an extended Kalman filter to the first coordinates detected by the first detection unit 21 and the second coordinates detected by the second detection unit 22 to estimate the position of the human body.

[0065] The position estimation process executed by the tracking processing unit 23 is not limited to the extended Kalman filter. For example, the position of the human body may be estimated by applying a particle filter to the first coordinates detected by the first detection unit 21 and the second coordinates detected by the second detection unit 22. As the position estimation process executed in the human body tracking process in the human body tracking device 100 of the present disclosure, known position estimation processes including the above-mentioned extended Kalman filter and particle filter can be applied.

[0066] 15A and 15B are conceptual diagrams showing the time transition of the first coordinate. 16A and 16B are conceptual diagrams showing the time transition of the second coordinate. 17A and 17B are conceptual diagrams showing the time transition of coordinates indicating an estimated human body position. Here, an example is shown in which a human body moves at a constant velocity from coordinates (0, y0) to coordinates (0, y1) from time T0 to time T1, comes to a standstill at coordinates (0, y1) from time T1 to time T2, and then moves at a constant velocity from coordinates (0, y1) to coordinates (0, y2) from time T2 to time T3.

[0067] 15A and 15B , from time T0 to time T1 and from time T2 to time T3 when the human body is moving, the coordinates of the moving object detected by the first detection unit 21 are input as first coordinates to the tracking processing unit 23. The coordinates of the stationary object are removed by the clutter removal unit 215 of the first detection unit 21.

[0068] 16A and 16B , from time T1 to time T2 when the human body is stationary, the coordinates of the human body detected by the second detection unit 22 are input as second coordinates to the tracking processing unit 23. At least the coordinates of objects other than the stationary human body (stationary objects other than the human body, moving human bodies) are excluded by peak extraction processing in the peak extraction unit 224 of the second detection unit 22.

[0069] 17A and 17B , the tracking processing unit 23 executes the position estimation process, thereby enabling tracking of the human body over the entire period from time T0 to time T3, including the time T0 to time T1 when the human body is moving, the time T1 to time T2 when the human body is stationary, and the time T2 to time T3 when the human body is moving. This enables continuous acquisition of vital signs in a configuration in which vital signs are acquired based on body surface displacements of the human body, such as heartbeats.

[0070] The above-described embodiments are provided to facilitate understanding of the present disclosure and are not intended to limit the present disclosure. The present disclosure may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present disclosure.

[0071] The present disclosure can have the following configurations as described above or instead of the above.

[0072] (1) A human body tracking device according to one aspect of the present disclosure includes a transceiver unit that receives reflected waves of transmitted radio waves, and a processing unit that estimates the position of a human body based on an IF signal output from the transceiver unit, wherein the processing unit includes a first detection unit that detects the coordinates of a moving object as first coordinates, a second detection unit that detects the coordinates of at least a stationary human body as second coordinates, and a tracking processing unit that tracks the human body based on the first coordinates and the second coordinates.

[0073] In this configuration, the first coordinates are detected when the human body being detected is moving, and the second coordinates are detected when the human body is stationary, thereby enabling continuous tracking of the human body.

[0074] (2) In the human body tracking device of (1) above, the second detection unit extracts, as the second coordinates, coordinates determined based on an element product of power distribution, amplitude variance, and correlation degree between amplitude and phase.

[0075] In this configuration, the coordinates of the human body having a high correlation between amplitude and phase are extracted as the second coordinates, thereby preventing the loss of a stationary human body.

[0076] (3) In the human body tracking device of (1) or (2) above, the first detection unit extracts the first coordinates by removing coordinates of stationary objects from coordinates of objects.

[0077] In this configuration, the coordinates of the moving object are detected as the first coordinates, which makes it possible to track a moving human body.

[0078] (4) In the human body tracking device according to (1) to (3) above, the tracking processing unit estimates the position of the human body using an extended Kalman filter or a particle filter.

[0079] This improves the accuracy of estimating the position of the human body.

[0080] (5) A human body tracking method according to one aspect of the present disclosure includes a transmission / reception step of receiving reflected waves of transmitted radio waves, and a processing step of estimating the position of the human body based on the IF signal output in the transmission / reception step, wherein the processing step includes a first step of detecting coordinates of a moving object as first coordinates, a second step of detecting coordinates of at least a stationary human body as second coordinates, and a third step of tracking the human body based on the first coordinates and the second coordinates.

[0081] In this configuration, the first coordinates are detected when the human body being detected is moving, and the second coordinates are detected when the human body is stationary, thereby enabling continuous tracking of the human body.

[0082] (6) In the human body tracking method of (5) above, in the second step, coordinates determined based on an element product of the power distribution, the amplitude variance, and the correlation degree between the amplitude and the phase are extracted as the second coordinates.

[0083] In this configuration, the coordinates of the human body having a high correlation between amplitude and phase are extracted as the second coordinates, thereby preventing the loss of a stationary human body.

[0084] (7) In the human body tracking method of (5) or (6) above, in the first step, the coordinates of the object are extracted by removing coordinates of stationary objects.

[0085] In this configuration, the coordinates of the moving object are detected as the first coordinates, which makes it possible to track a moving human body.

[0086] (8) In the human body tracking method according to (5) to (7) above, in the third step, the position of the human body is estimated using an extended Kalman filter or a particle filter.

[0087] This improves the accuracy of estimating the position of the human body.

[0088] The present disclosure makes it possible to realize a human body tracking device and a human body tracking method that can improve the accuracy of tracking a human body.

[0089] REFERENCE SIGNS LIST 1 Transmitter / receiver 2 Processing unit 21 First detector 22 Second detector 23 Tracking processor 100 Human body tracking device 211 1D-FFT processor 212 2D-FFT processor 213 Peak detector 214 Angle estimator 215 Clutter remover 221 Power distribution calculator 222 Storage unit 223 Calculator 224 Peak extractor

Claims

1. A human body tracking device comprising: a transceiver unit that receives reflected waves of transmitted radio waves; and a processing unit that estimates the position of a human body based on an IF signal output from the transceiver unit, wherein the processing unit comprises: a first detection unit that detects the coordinates of a moving object as a first coordinate; a second detection unit that detects the coordinates of at least a stationary human body as a second coordinate; and a tracking processing unit that tracks the human body based on the first coordinates and the second coordinates.

2. A human body tracking device as described in claim 1, wherein the second detection unit extracts coordinates determined based on an element product of power distribution, amplitude variance, and correlation degree between amplitude and phase as the second coordinates.

3. A human body tracking device according to claim 1 or 2, wherein the first detection unit extracts the first coordinates by removing coordinates of stationary objects from the coordinates of the objects.

4. A human body tracking device according to any one of claims 1 to 3, wherein the tracking processing unit estimates the position of the human body using an extended Kalman filter or a particle filter.

5. A human body tracking method comprising: a transmission / reception step of receiving reflected waves of a transmitted radio wave; and a processing step of estimating a position of a human body based on an IF signal output in the transmission / reception step, wherein the processing step includes: a first step of detecting coordinates of a moving object as first coordinates; a second step of detecting coordinates of at least a stationary human body as second coordinates; and a third step of tracking the human body based on the first coordinates and the second coordinates.

6. A human body tracking method according to claim 5, wherein in the second step, coordinates determined based on an element product of power distribution, amplitude variance, and correlation degree between amplitude and phase are extracted as the second coordinates.

7. A human body tracking method according to claim 5 or 6, wherein in the first step, coordinates of stationary objects are removed from coordinates of objects to extract the first coordinates.

8. The human body tracking method according to any one of claims 5 to 7, wherein in the third step, the position of the human body is estimated using an extended Kalman filter or a particle filter.

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