Biological detection device

Abstract

To provide a biological detection device that can improve the accuracy of counting the number of living organisms within an observation range. [Solution] The transmitting and receiving unit receives the reflected waves of radio waves transmitted from at least one transmitting antenna to the observation range using multiple receiving antennas, and generates an intermediate frequency signal based on the transmitted signal and the received signal. The processing unit counts the location and number of living organisms within the observation range based on the intermediate frequency signal. The processing unit calculates a complex signal for each location within the observation range based on the intermediate frequency signal, which reflects the presence or absence of an object and the velocity of the object. Furthermore, based on the complex signal, it calculates a living organism determination index for each location within the observation range, whose size differs depending on whether the detected object is a living organism or not, and whose size depends on the number of living organisms. The location of living organisms within the observation range is identified and the number of living organisms is counted using the living organism determination index.

Description

[Technical Field]

[0001] This invention relates to a biological detection device. [Background technology]

[0002] A device for detecting living organisms using radar is known (Patent Document 1). The device described in Patent Document 1 receives multiple reflected signals reflected by at least one person within the observation range. Based on these multiple reflected signals, multiple vital signals relating to at least one person within the observation range are acquired, and the similarity between the multiple vital signals is calculated. Based on the similarity, it is determined whether the multiple vital signals relate to one person or to multiple people. This suppresses false detection of the number of people. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2024-128762 [Overview of the project] [Problems that the invention aims to solve]

[0004] When multiple individuals are in close proximity, below the angular resolution of the radar, the similarity of their vital signals increases due to the influence of multipath waves passing through them. As a result, even though multiple people are present, they may be mistakenly identified as a single individual. The objective of the present invention is to provide a biological detection device that can improve the accuracy of counting the number of living organisms within the observation range. [Means for solving the problem]

[0005] According to one aspect of the present invention, A transmitting and receiving unit that receives reflected waves of radio waves transmitted from at least one transmitting antenna to an observation range using multiple receiving antennas, and generates an intermediate frequency signal based on the transmitted signal and the received signal, A processing unit that identifies the position of a living body within the observation range and counts the number of living bodies based on the intermediate frequency signal is provided with The processing unit calculates a complex signal that reflects the presence or absence of an object and the velocity of the object for each position within the observation range based on the intermediate frequency signal calculates a living body determination index that differs in magnitude depending on whether the detected object is a living body and whose magnitude depends on the number of living bodies for each position within the observation range based on the complex signal uses the living body determination index to identify the position of the living body within the observation range and count the number of living bodies death, The aforementioned processing unit, When identifying the location of living organisms within the observation range and counting the number of living organisms, the location of living organisms is identified based on the distribution of the living organism determination index for each location within the observation range. The sum of the biological determination indices in at least a portion of the area within the observation range is calculated. Based on the sum of the aforementioned biological assessment indicators, the number of living organisms is counted. The processing unit uses an index as the biological determination index that includes the strength of the correlation between the time change of the amplitude component of the complex signal and the time change of the phase component of the complex signal for each position within the observation range. A living body detection device is provided According to another aspect of the present invention, A transmitting and receiving unit that receives reflected waves of radio waves transmitted from at least one transmitting antenna to an observation range using multiple receiving antennas, and generates an intermediate frequency signal based on the transmitted signal and the received signal, A processing unit that identifies the location of living organisms within the observation range and counts the number of living organisms based on the aforementioned intermediate frequency signal. Equipped with, The aforementioned processing unit, Based on the aforementioned intermediate frequency signal, a complex signal is calculated for each position within the observation range that reflects the presence or absence of an object and the velocity of the object. Based on the complex signal, for each position within the observation range, a biological determination index is calculated, the size of which differs depending on whether the detected object is a living organism or not, and the size of which depends on the number of living organisms. Using the aforementioned biological identification index, the location of living organisms within the observation range is identified, and the number of living organisms is counted. The aforementioned processing unit, In the process of identifying the location of living organisms within the aforementioned observation range and counting the number of living organisms, From within the observation range, extract a point consisting of a point where either the absolute value of the complex signal or the biometric indicator shows a local peak, and a point cloud consisting of multiple points around the point showing the local peak. Based on the extracted point cloud, the location of the organism is identified, and the number of organisms is counted. The processing unit provides a biological detection device that uses an index as the biological determination index that includes the strength of the correlation between the time change of the amplitude component of the complex signal and the time change of the phase component of the complex signal for each position within the observation range.

Advantages of the Invention

[0006] The number of living bodies can be counted from the magnitude of the living body determination index whose magnitude depends on the number of living bodies. Therefore, the counting accuracy of the number of a plurality of living bodies existing in proximity is improved compared to the spatial resolution of object detection

Brief Description of the Drawings

[0007] [Figure 1] Figure 1 is a block diagram of a biodetection device according to the first embodiment. [Figure 2] Figure 2 is a flowchart showing the processing procedures performed by the transmitting / receiving unit 20 and the processing unit 30. [Figure 3] Figure 3 is a schematic diagram showing the arrangement of various pieces of information (data) required in the processing procedure of step S3 (Figure 2). [Figure 4] Figure 4 is a flowchart showing the detailed processing steps for step S5 (Figure 2). [Figure 5] Figure 5 is a graph showing an example of the relationship between the number of living organisms and the magnitude of the biological assessment index I. [Figure 6] Figure 6A is a schematic plan view showing the arrangement of objects within the observation range 50, Figure 6B is a diagram showing the power distribution obtained by squaring the absolute value of the complex signal obtained in step S3 (Figure 2) using shades of gray, and Figure 6C is a diagram showing the distribution of the biological determination index I obtained in step S4 (Figure 2) using shades of gray. [Figure 7] Figure 7 is a flowchart showing the processing procedure performed by the transmitting / receiving unit 20 and the processing unit 30 of the biological detection device according to the second embodiment. [Figure 8] Figure 8 shows the power distribution obtained by squaring the absolute value of the complex signal obtained in step S3 (Figure 7) using shades of gray, and also illustrates the state in which the observation range 50 is divided into four sections D1, D2, D3, and D4. [Figure 9] Figure 9 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the amplitude component m(x,y,t) and the phase component p(x,y,t) from the three-dimensional data X(r,d,n) in the third embodiment. [Figure 10] Figure 10 is a flowchart showing the processing procedure performed by the transmitting / receiving unit 20 and the processing unit 30 of the biological detection device according to the third embodiment. [Figure 11] Figure 11 is a flowchart showing the detailed steps of step SB5 (Figure 10). [Figure 12]Figure 12 is a schematic diagram showing an example of a point cloud 40 distributed over the observation range 50, i.e., the floor surface (xy plane). [Figure 13] Figures 13A through 13E are graphs showing the time evolution of various cluster features. [Figure 14] Figure 14A is a schematic diagram showing an example of the arrangement of clusters 45A and 45B when two clusters 45A and 45B are defined within the observation range 50 in step SB52 (Figure 11), Figure 14B is a schematic diagram illustrating an example of a method for determining the location of a living organism, and Figure 14C is a schematic diagram illustrating another example of a method for determining the location of a living organism. [Figure 15] Figure 15 is a schematic diagram illustrating yet another example of a method for determining the location of a living organism. [Figure 16] Figure 16 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the amplitude component m(x,y,z) and phase component p(x,y,z), which are three-dimensional data, from the three-dimensional data X(r,d,n) in the fourth embodiment. [Figure 17] Figure 17 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining a complex signal P(r,θ,φ) from three-dimensional data X(r,d,n). [Figure 18] Figure 18 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the biological complex signal P'(r,θ,φ) from the biological determination index I'(r,d,n). [Modes for carrying out the invention]

[0008] [First Embodiment] The first embodiment of the biological detection device will be described with reference to the drawings from Figure 1 to Figure 6C. Figure 1 is a block diagram of a biological detection device according to the first embodiment. The biological detection device according to the first embodiment includes a transmitting / receiving unit 20 and a processing unit 30.

[0009] The transmitting / receiving unit 20 includes a signal generating unit 22, a plurality of AD converters 23, a plurality of mixers 24, at least one transmitting antenna Tx, and a plurality of receiving antennas Rx.

[0010] The signal generator 22 periodically supplies a transmission signal called a chirp to the transmitting antenna Tx, which linearly increases in frequency over time. The transmitting antenna Tx transmits radio waves within the observation range 50. Each of the multiple receiving antennas Rx receives reflected waves from objects 51 within the observation range 50.

[0011] Mixers 24, each prepared for multiple receiving antennas Rx, mix the transmitted signal and the received signal to generate an intermediate frequency signal (IF signal). AD converters 23 perform AD conversion on each of the IF signals. The AD-converted IF signals are input to the processing unit 30.

[0012] The processing unit 30 estimates the position and velocity of the object within the observation range 50 based on the IF signal, determines whether the detected object 51 is a living organism or not, and counts the number of living organisms within the observation range 50.

[0013] Next, the functions of the processing unit 30 will be explained with reference to Figure 2. Figure 2 is a flowchart showing the processing steps performed by the processing unit 30.

[0014] First, the transmitting / receiving unit 20 transmits and receives signals (step S1). The transmitted signal is a frequency continuous modulation wave in which chirps, in which the frequency changes linearly with respect to time, repeatedly appear. The mixer 24 and the AD converter 23 generate an IF signal for each channel, which is composed of multiple pairs of transmitting antenna Tx and receiving antenna Rx (step S2).

[0015] Next, the processing unit 30 performs distance FFT, velocity FFT, and angle estimation processing on the IF signal to calculate the distribution of the complex signal within the observation range 50 and the power obtained by squaring the absolute value of this complex signal (step S3). This processing is the same as that of a known FMCW radar system. For example, a distance FFT is performed for each chirp, and a velocity FFT is performed on the result of the distance FFT processing for each chirp frame consisting of multiple chirps. Angle estimation processing is performed on the result of the velocity FFT processing for multiple channels. The calculated complex signal is represented by a complex number that reflects the presence or absence of an object and the velocity of the object at each position within the observation range 50, and the power is represented by the square of the absolute value of the complex signal.

[0016] The process of step S3 will be explained with reference to Figure 3. Figure 3 is a schematic diagram showing the arrangement of various information (data) obtained in the processing procedure of step S3. By performing distance FFT processing and velocity FFT processing, three-dimensional data X(r,d,n) defined in the three-dimensional space of distance r, velocity d, and channel n is calculated.

[0017] Next, the processing unit 30 averages the three-dimensional data X in the velocity direction. This yields two-dimensional data X'(r,n) defined in a two-dimensional space of distance r and channel n. By performing angle estimation processing based on the two-dimensional data X'(r,n), a complex signal P(r,θ) defined in a two-dimensional space of distance r and angle θ is calculated. The complex signal P is a complex number and includes an amplitude component m and a phase component p. The complex signal P reflects the presence or absence of an object and the velocity of the object. The position of an object can be detected from the complex signal P or the power distribution. The complex signal P(r,θ) can be considered as a signal that reflects the amplitude and phase of the reflected wave from position (r,θ).

[0018] The processing unit 30 calculates the amplitude component m(r,θ) and phase component p(r,θ) of the complex signal P(r,θ) from the complex signal P(r,θ). By calculating the amplitude component m(r,θ) and phase component p(r,θ) for each of the multiple chirp frames and saving the calculation results, the amplitude component m(r,θ,t) and phase component p(r,θ,t) of the complex signal P can be obtained for each distance r, angle θ, and discretized time t.

[0019] In step S3 (Figure 2), once the complex signal distribution, such as the amplitude component m(r,θ,t) and the phase component p(r,θ,t), is determined, the processing unit 30 calculates a biological determination index I(r,θ) for each position (r,θ) (step S4).

[0020] Next, the biological identification index I(r,θ) will be explained. The surface of a living organism is periodically displaced by respiration, heartbeat, etc. When a living organism is present at a specific location (r,θ), the amplitude component m(r,θ,t) and phase component p(r,θ,t) at that location will largely reflect characteristics caused by the periodic displacement of the body surface. For example, the time change of the amplitude component m(r,θ,t) and the time change of the phase component p(r,θ,t) will show the same trend, and the correlation between the two will become stronger. The biological identification index I(r,θ) is defined such that when the characteristics caused by the periodic displacement of the body surface are largely reflected in the amplitude component m(r,θ,t) and phase component p(r,θ,t), the biological identification index I(r,θ) becomes large.

[0021] When a stationary object other than a living organism is present at a specific position (r,θ), the amplitude component m(r,θ,t) and phase component p(r,θ,t) do not exhibit features caused by the periodic displacement of the body surface. Random noise components are superimposed on the time evolution of the amplitude component m(r,θ,t) and phase component p(r,θ,t), weakening the correlation between the two.

[0022] Furthermore, the amplitude of the time variation of the amplitude component m caused by displacement of the body surface is greater than the amplitude of the time variation of the noise component. For this reason, the variance of the amplitude component m(r,θ,t) when a living organism is present at a specific position (r,θ) is greater than the variance of the amplitude component m(r,θ,t) when a stationary object is present.

[0023] By using the correlation between the time evolution of the amplitude component m(r,θ,t) and the time evolution of the phase component p(r,θ,t), and the aforementioned properties of the variance of the amplitude component m(r,θ,t), it is possible to determine whether the detected object is a living organism or not.

[0024] The strength of the correlation C between the time evolution of the amplitude component m(r,θ,t) and the phase component p(r,θ,t). mp This is defined by the following formula.

number

[0025] Variance σ of the amplitude component m(r,θ,t) m 2 This can be calculated, for example, using the following formula.

number

[0026] A biological diagnostic index I(r,θ,t) is defined, for example, by the following formula.

number

[0027] In step S4 (Figure 2), once the biological identification index I(r,θ,t) is calculated, the processing unit 30 uses the biological identification index I(r,θ,t) to identify the location of the organism and counts the number of organisms. For example, the processing unit 30 identifies the location of the organism and counts the number of organisms based on the spatial distribution of the biological identification index I(r,θ,t) and the sum of the biological identification index I(r,θ,t) within the observation range 50 (step S5).

[0028] Next, with reference to Figure 4, the detailed processing steps of step S5 (Figure 2) will be described. Figure 4 is a flowchart showing the detailed processing steps of step S5.

[0029] The processing unit 30 identifies the location of living organisms from the distribution of the biological detection index I(r,θ,t) and counts the number of living organisms (step S51). For example, it detects the peak position of the distribution of the biological detection index I(r,θ,t). If the peak value exceeds a predetermined threshold, it can be estimated that a living organism is present at that peak position. This threshold can be set to a value that is sufficiently larger than the average value of the biological detection index I in areas where no living organisms exist, for example, 100 times the average value, obtained in advance. Alternatively, the threshold can be set by applying the known CA-CFAR (Cell-Averaging Constant False Alarm Rate) algorithm. The number of living organisms can be counted by counting the number of peaks.

[0030] Next, the processing unit 30 calculates the sum of the biological determination index I in the observation range 50 (step S52). Then, based on the calculated sum of the biological determination index I, the number of organisms counted in step S51 is corrected (step S53). In the organism counting process in step S51, if multiple organisms are located close together compared to the spatial resolution of the peak position detection of the distribution of biological determination index I, the number of organisms is counted as 1. In step S53, the number of organisms located at a single peak position is counted more accurately.

[0031] Next, with reference to Figure 5, we will explain the method for correcting the number of organisms. As shown in equation (3), the correlation C mp The variance becomes stronger, m 2 As the value of increases, the biological identification index I also increases. The biological identification index I is larger when there are multiple organisms at the peak position than when there is one. By conducting various evaluation experiments in advance, the relationship between the number of organisms and the magnitude of the biological identification index I can be determined.

[0032] Figure 5 is a graph showing an example of the relationship between the number of organisms and the magnitude of the organism identification index I. The horizontal axis represents the sum of the organism identification index I, and the vertical axis represents the number of organisms. Various evaluation experiments have shown that the sum of the organism identification index I is approximately proportional to the number of organisms. For example, when the number of organisms is 1, the sum of the organism identification index I is approximately 2, and when the number of organisms is 2, the sum of the organism identification index I is approximately 4. Knowing the relationship shown in Figure 5 beforehand, the number of organisms can be determined from the sum of the organism identification index I. In step S53, the number of organisms counted in step S51 is corrected to the number of organisms determined based on the relationship shown in Figure 5.

[0033] Next, with reference to Figures 6A, 6B, and 6C, the results of an evaluation experiment to detect living organisms within the observation range 50 will be described. Figure 6A is a schematic plan view showing the arrangement of objects within the observation range 50. A biological detection device 55 is positioned approximately in the center of one of the shorter sides of the rectangular observation range 50. Two people 53 are located in close proximity in front of the biological detection device 55, and a stationary object 52 is located at approximately the same distance as the people 53. A wall 54 is located further away from the people 53 and the stationary object 52.

[0034] Figure 6B shows the power distribution obtained from the square of the absolute value of the complex signal obtained in step S3 (Figure 2), using shades of gray. Regions with relatively high power are shown in darker shades. In regions 53A, 52A, and 54A, which correspond to the person 53, the stationary object 52, and the wall 54, the power is relatively high. At this stage, since it has not been determined whether the objects are living organisms or not, the person 53, the stationary object 52, and the wall 54 are detected.

[0035] FIG. 6C is a diagram showing the distribution of the biological determination index I obtained in step S4 (FIG. 2) in terms of gray scale. The biological determination index I at the positions of regions 52A and 54A (FIG. 6B) corresponding to the stationary object 52 and the wall 54 is small, and the biological determination index I in the region 53A corresponding to the person 53 is relatively large. Thereby, the complex signal P (FIG. 3) and its power caused by the stationary object 52 or the like can be suppressed, and a living body can be detected.

[0036] However, since there is only one peak position in the distribution of the biological determination index I shown in FIG. 6C, the number of living bodies is counted as 1 in step S51 (FIG. 4). Thereafter, by executing steps S52 and S53, the number of living bodies is corrected to 2. In this way, the number of living bodies can be accurately counted.

[0037] Next, the excellent effects of the first embodiment will be described. In the first embodiment, by correcting the number of living bodies in step S53 (FIG. 4), even when a plurality of living bodies are present in proximity, the counting accuracy of the living bodies can be improved.

[0038] Next, a modified example of the first embodiment will be described. In the first embodiment, as shown in Equation (3), as the biological determination index I for determining whether an object is a living body, the square of the absolute value (power) of the complex signal P, the strength c of the correlation mp , and the variance σ m 2 are used for calculation. However, as the biological determination index I, the square of the absolute value (power) of the complex signal P is not used, and the strength c of the correlation mp and the variance σ m 2 may be used.

[0039] Furthermore, the reason for using the variance σ m 2 in the biological determination index I is that attention is paid to the fact that the variance of the amplitude caused by the displacement of the body surface of a living body is larger than the variance of the amplitude of random noise. In order to determine whether an object is a living body, the variance σm 2 This is not mandatory. For example, the strength of correlation c as a biological judgment index I. mp Only this may be adopted.

[0040] In addition, as a biological identification index I, an index may be adopted in which the size of the detected object differs depending on whether it is a living organism or not, and the size depends on the number of living organisms, for each location within the observation range 50.

[0041] The above embodiment shows an example of detecting a living organism using an FMCW radar, but other types of radar may be used. For example, Doppler radar, pulse radar, OFDM (Orthogonal Frequency Division Multiplexing) radar, etc. may be used. In this case, among the various information obtained from the received signal, an index that reflects the periodic displacement of the body surface caused by the living organism's respiration and heartbeat may be used as the biological identification index.

[0042] [Second Example] Next, the biodetection device according to the second embodiment will be described with reference to Figures 7 and 8. The following description will omit explanations of components common to the biodetection device according to the first embodiment, which was described with reference to Figures 1 through 6C.

[0043] Figure 7 is a flowchart showing the processing procedure performed by the transmitting / receiving unit 20 and the processing unit 30 of the biological detection device according to the second embodiment. The procedure from step S1 to step S4 is the same as the procedure from step S1 to step S4 in the first embodiment (Figure 2). When a biological determination index I is calculated at each position within the observation range 50, the processing unit 30 uses the biological determination index I to identify the location of the biological organism. For example, the processing unit 30 identifies the location of the biological organism based on the distribution of the biological determination index I (step SA5). Subsequently, the processing unit 30 divides the observation range 50 into multiple sections (step SA6).

[0044] Figure 8 shows the distribution of the biological detection index I obtained in step SA5 (Figure 7) using shades of gray, and also illustrates the state in which the observation range 50 is divided into four sections D1, D2, D3, and D4. The area 53A corresponding to the person 53 (Figure 6A) is located within section D4. When dividing into multiple sections, the sections are determined so that the boundary lines of the sections do not pass through the location where a biological organism is detected, for example, the dark gray area 53A.

[0045] The processing unit 30 divides the observation range 50 into multiple sections, and then counts the number of living organisms in each section using the biological identification index I. For example, the processing unit 30 counts the number of living organisms based on the sum of the biological identification index I in each section (step SA7). The process in step SA7 is the same as the process in step S53 (Figure 4) performed by the biological detection device according to the first embodiment. Step SA7 is repeated until the counting process is completed for all sections (step SA8).

[0046] In the example shown in Figure 8, the count of living organisms is zero in sections D1, D2, and D3, and the count of living organisms is 2 in section D4.

[0047] Next, we will describe the excellent effects of the second embodiment. In the second embodiment, as in the first embodiment, the accuracy of counting living organisms can be improved even when multiple living organisms are present in close proximity.

[0048] If the IF signal generated in step S2 contains disturbances, noise is superimposed on the biological detection index I. When the range over which the sum of biological detection index I is calculated is wide, more noise is added to the sum. As a result, the accuracy of the count result for the number of living organisms decreases. As in the second embodiment, when the range over which the sum of biological detection index I is calculated is narrowed, the sum becomes less susceptible to noise, and thus the decrease in the accuracy of the count result is suppressed.

[0049] Furthermore, in the second embodiment, the number of living organisms in each section can be counted. For example, if living organisms are detected in two locations within the observation range 50, in the first embodiment, the total number of living organisms present in the two locations can be counted, but the number of living organisms in each of the two locations cannot be counted individually. In the second embodiment, if the two locations where living organisms are detected are divided into different sections, the number of living organisms in each of the two locations can be counted individually.

[0050] [Third Embodiment] Next, a biodetection device according to the third embodiment will be described with reference to Figures 9 to 15. The following description will omit details of components common to the biodetection device according to the first embodiment, which was described with reference to Figures 1 to 6C.

[0051] In the first embodiment, as shown in Figure 6A, radio waves are emitted almost horizontally from a biodetector 55 mounted on the wall of a room. In the third embodiment, however, radio waves are emitted toward the floor from a biodetector 55 mounted on the ceiling of a room, for example. In the third embodiment, the observation range is the floor, and the position and number of living organisms present on the floor are detected. The biodetection method according to the third embodiment can also be applied when radio waves are emitted horizontally, as exemplified in the first embodiment.

[0052] Figure 9 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the amplitude component m(x,y,t) and phase component p(x,y,t) of a complex signal P(x,y) from three-dimensional data X(r,d,n). The three-dimensional data X(r,d,n) is the same as the three-dimensional data X(r,d,n) shown in Figure 3 of the first embodiment. In the first embodiment, as shown in Figure 3, the amplitude component m(r,θ,t) and phase component p(r,θ,t) are defined in polar coordinates, but in the third embodiment, the amplitude component m(x,y,t) and phase component p(x,y,t) are defined in the xy Cartesian coordinate system.

[0053] Furthermore, in order to acquire information within the floor surface of the observation range, the transmitting antenna Tx and the receiving antenna Rx are arranged in two dimensions. For example, at least one of the vectors from the transmitting antenna Tx to the receiving antenna Rx that constitute each of the multiple channels has an x-direction component parallel to the floor surface, and at least one other has a y-direction component parallel to the floor surface and perpendicular to the x-direction.

[0054] Similar to the first embodiment, the three-dimensional data X(r,d,n) is averaged in the velocity direction (d direction) to obtain the two-dimensional data X'(r,n). An angle estimation process is performed based on the two-dimensional data X'(r,n) to calculate a complex signal P(r,θ,φ) defined in three-dimensional space, represented by distance r, angle with respect to the z axis (polar angle) φ, and azimuth angle θ in the xy plane. The complex signal P(r,θ,φ) is accumulated in the φ direction to calculate a complex signal P(r,θ) defined in two-dimensional space. The complex signal P(r,θ) is converted into a complex signal P(x,y) expressed in xy Cartesian coordinates. Here, the x and y directions are parallel to the floor.

[0055] The amplitude component m(x,y) and phase component p(x,y) of the complex signal P(x,y) are calculated. The amplitude component m(x,y) and phase component p(x,y) are calculated for each of the multiple chirp frames. Through these processes, the amplitude component m(x,y,t) and phase component p(x,y,t) are obtained for each discretized time t.

[0056] Figure 10 is a flowchart showing the processing procedure performed by the transmitting / receiving unit 20 (Figure 1) and processing unit 30 (Figure 1) of the biological detection device according to the third embodiment. The procedure from step S1 to step S4 is the same as the procedure from step S1 to step S4 of the first embodiment (Figure 3). The complex signal and power distribution calculated in step S3 are defined in the xy plane parallel to the floor surface, as shown in Figure 9. In the first embodiment, the biological determination index I calculated in step S4 is defined in polar coordinates (r,θ) and time t, as shown in equation (3), but in the third embodiment, the biological determination index I is defined in xy coordinates (x,y) and time t.

[0057] In the third embodiment, the location of a living organism is identified and the number of living organisms is counted using the power of the biological identification index I(x,y,t) and the complex signal P(x,y).

[0058] Referring to Figure 11, the detailed procedure of step SB5 (Figure 10) will be described. Figure 11 is a flowchart showing the detailed procedure of step SB5. First, the processing unit 30 extracts a point cloud consisting of points in the xy plane where the biological determination index I(x,y,t) shows a local peak at a specific time, and multiple points around those points (step SB51). Points showing local peaks can be extracted using, for example, a known CFAR algorithm. Alternatively, a point cloud consisting of points where the power of the complex signal (x,y) shows a local peak, and multiple points around those points, may be extracted. In this case, points showing local peaks in regions determined to be stationary by the biological determination index I(x,y,t) are not extracted.

[0059] Next, the procedure for step SB51 will be described with reference to Figure 12. Figure 12 is a schematic diagram showing an example of a point cloud 40 distributed in the observation range 50, i.e., the floor surface (xy plane). First, points 41P where the power of the complex signal P(x,y) with signals from stationary objects removed shows a local peak are extracted. In Figure 12, points 41P showing a local peak are represented by black-filled circles. In the example shown in Figure 12, four points 41P showing a local peak are extracted. For example, constant false alarm probability (CFAR) processing can be used to extract points 41P showing a local peak.

[0060] Once a point 41P representing a local peak is extracted, several points 41A surrounding point 41P are extracted. In Figure 12, the surrounding points 41A are indicated by white circles. Various methods can be applied to extract the surrounding points 41A. For example, points where the power of the complex signal P(x,y) exceeds the threshold obtained from the CFAR process can be extracted as surrounding points 41A.

[0061] In addition, points 41A can be extracted as surrounding points 41A if their distance from point 41P, which exhibits a local peak, is less than a certain value. Furthermore, points 41A can also be extracted as surrounding points 41A if their signal-to-noise ratio (SNR) obtained from the complex signal P(x,y) is greater than or equal to a certain value.

[0062] After extracting the point cloud 40 (Figure 12) in step SB51 (Figure 11), clustering is performed on the point cloud 40 (step SB52). For example, clustering is performed based on the x and y coordinates of multiple points 41P, 41A, etc., included in the point cloud 40 extracted in step SB51. For clustering, well-known algorithms such as DBSCAN, k-means, x-means, etc., can be used.

[0063] Clustering is performed to classify the point cloud 40 into one or more clusters. In Figure 12, the point cloud is classified into three clusters 45A, 45B, and 45C. In the example shown in Figure 12, clusters 45A and 45B each contain one point 41P representing a local peak and multiple points 41A surrounding it. Cluster 45C contains two points 41P representing local peaks and multiple points 41A surrounding them.

[0064] After performing clustering in step S52 (Figure 11), the number of organisms in each cluster is determined based on the features of each cluster (step S53). It is advisable to use features that have different values ​​depending on the number of organisms present in the range where the points included in the cluster are distributed (hereinafter sometimes simply referred to as the number of organisms in the cluster). Next, the cluster features will be explained with reference to the diagrams from Figure 13A to Figure 13E.

[0065] Figures 13A to 13E are graphs showing the time evolution of various cluster features. The features shown in these graphs were calculated based on the complex signal P obtained during evaluation experiments to actually detect living organisms. The horizontal axis represents the serial number assigned to the chirp frame, and the vertical axis represents the feature value. In other words, the horizontal axis corresponds to the passage of time. The solid lines in the graphs from Figure 13A to 13E represent the features of clusters with 2 living organisms, and the dashed lines represent the features of clusters with 1 living organism.

[0066] Figure 13A shows the time evolution of the feature when the total power of the complex signal P is used as the feature. The vertical axis represents the normalized value of the total power of the points included in the cluster. The total power of the cluster with 2 organisms is greater than the total power of the cluster with 1 organism. This result suggests that as the number of organisms in each of the clusters 45A, 45B, and 45C increases, the total power of each of the clusters 45A, 45B, and 45C also increases.

[0067] Figure 13B shows the time evolution of the feature when the average power of the complex signal P is used as the feature. The vertical axis represents the normalized value of the average power of all points included in the cluster. The average power of the cluster with 2 organisms is greater than the average power of the cluster with 1 organism. This result suggests that as the number of organisms in each of the clusters 45A, 45B, and 45C increases, the total power of each of the clusters 45A, 45B, and 45C also increases.

[0068] Figure 13C shows the time evolution of the feature when the number of points in a cluster is used as the feature. The vertical axis represents the normalized value of the number of points in a cluster. The number of points in a cluster with 2 organisms is greater than the number of points in a cluster with 1 organism. This result suggests that as the number of organisms in each of the clusters 45A, 45B, and 45C increases, the number of points constituting each of the clusters 45A, 45B, and 45C also increases.

[0069] Figures 13D and 13E show the time evolution of features when the standard deviation of the x-coordinate and y-coordinate of points included in the clusters are used as features, respectively. The vertical axis represents the normalized values ​​of the standard deviation of the x-coordinate and y-coordinate of points included in the clusters. The standard deviation of the coordinates of points included in clusters with 2 organisms is greater than the standard deviation of the coordinates of points included in clusters with 1 organism. This result suggests that as the number of organisms in each of clusters 45A, 45B, and 45C increases, the standard deviation of the x-coordinate and y-coordinate of points included in each of clusters 45A, 45B, and 45C also increases. It can also be said that as the number of organisms in each of clusters 45A, 45B, and 45C increases, the variance of the x-coordinate and y-coordinate of points included in each of clusters 45A, 45B, and 45C also increases.

[0070] As explained with reference to Figures 13A to 13E, the total power, average power, number of points, standard deviation (or variance) of the x-coordinate, and standard deviation (or variance) of the y-coordinate of a cluster depend on the number of organisms in the cluster. Therefore, by setting a threshold for the features and comparing the calculated features with the threshold, the number of organisms in the cluster can be estimated.

[0071] As explained with reference to Figures 13A to 13E, the features of a cluster fluctuate over time. To avoid the effects of time-dependent fluctuations, it is preferable to determine the number of organisms using the average value of the features over a certain period of time. Furthermore, it is advisable to determine the number of organisms based on multiple features. By using multiple features, the accuracy of the determination can be improved.

[0072] After determining the number of organisms in step SB53 (Figure 11), the processing unit 30 determines the location of organisms in each of the clusters 45A, 45B, and 45C (Figure 12) (step SB54). For example, the processing unit 30 determines the location of organisms in each of the clusters 45A, 45B, and 45C based on the distribution of multiple points contained in each of the clusters 45A, 45B, and 45C.

[0073] Next, various examples of methods for determining the position of a living organism will be described with reference to Figures 14A to 15.

[0074] Figure 14A is a schematic diagram showing an example of the arrangement of clusters 45A and 45B when the point cloud within the observation range 50 is classified into two clusters, 45A and 45B, in step SB52 (Figure 11). The observation range 50 is divided into multiple sections Di. The point cloud constituting one cluster 45A is distributed within one section Da. The point cloud constituting the other cluster 45B is distributed across two sections Db and Dc. The centroids Ga and Gb of the point clouds constituting clusters 45A and 45B are located within sections Da and Db, respectively. In step SB53 (Figure 11), it is determined that the number of living organisms present in clusters 45A and 45B shown in Figure 14A is 1 and 2, respectively.

[0075] Figure 14B is a schematic diagram illustrating an example of a method for determining the location of living organisms. In the example shown in Figure 14B, the processing unit 30 determines that the number of living organisms in the cluster is present in the section Di which contains the centroids of the point clouds that make up the cluster. In the example shown in Figure 14A, since the centroid Ga of the point cloud of cluster 45A is located within section Da, the processing unit 30 determines that the number of living organisms in section Da is 1, which is the number of living organisms in cluster 45A. Since the centroid Gb of the point cloud of cluster 45B is located within section Db, the processing unit 30 determines that the number of living organisms in section Db is 2, which is the number of living organisms in cluster 45B. In this case, some points of the point cloud of cluster 45B are also distributed within section Dc, but the number of living organisms in section Dc is determined to be 0.

[0076] Figure 14C is a schematic diagram illustrating another example of a method for determining the location of living organisms. In the example shown in Figure 14C, it is determined that living organisms are distributed in one or more compartments Di where the point cloud constituting the cluster is distributed. In the example shown in Figure 14A, since the point cloud constituting cluster 45A is distributed only within compartment Da, the processing unit 30 determines that the number of living organisms in compartment Da is 1, which is the number of living organisms in cluster 45A. Since the point cloud constituting cluster 45B is distributed across two compartments Db and Dc, the processing unit 30 determines that living organisms exist in compartments Db and Dc. Furthermore, since the number of living organisms in cluster 45B is 2, the processing unit 30 determines that the number of living organisms in compartments Db and Dc is 1 each.

[0077] If a point cloud constituting a cluster with 1 organism is distributed across multiple partitions Di, it is advisable to determine one of these partitions Di as the partition where the organism resides. For example, the partition Di where the centroid of the point cloud is located, or the partition Di containing the most points, can be determined as the partition where the organism resides.

[0078] If the number of organisms in a cluster is greater than the number of partitions Di in which the point cloud constituting the cluster is distributed, it is advisable to allocate two or more organisms to one of the partitions Di. The allocation of organisms can be weighted, for example, by the number of points present in each partition Di.

[0079] If the number of organisms in a cluster is less than the number of compartments Di in which the point clouds constituting the cluster are distributed, it is advisable to set the number of adults in any of the compartments Di to 0. For example, the number of adults in the compartments Di that contain the fewest points in each compartment Di should be set to 0.

[0080] Figure 15 is a schematic diagram illustrating yet another example of a method for determining the location of living organisms. In the example shown in Figure 15, the observation range 50 is not divided into sections, and the location of living organisms is defined by coordinates. For example, the processing unit 30 determines that a living organism exists at the centroid of the point cloud constituting a cluster. For example, the processing unit 30 determines that a living organism exists at the centroid Ga of the point cloud constituting cluster 45A, and determines the number of living organisms to be 1, which is the number of living organisms in cluster 45A. Also, the processing unit 30 determines that a living organism exists at the centroid Gb of the point cloud constituting cluster 45B, and determines the number of living organisms to be 2, which is the number of living organisms in cluster 45B.

[0081] Next, we will describe the excellent effects of the third embodiment. In the third embodiment, as in the first embodiment, the position and number of living organisms can be determined by excluding stationary objects.

[0082] [Fourth embodiment] Next, a biodetection device according to the fourth embodiment will be described with reference to Figure 16. The following description will omit details of components common to the biodetection device according to the third embodiment, which was described with reference to Figures 9 through 15.

[0083] In the third embodiment (Figure 9), the amplitude component m(x,y) and phase component p(x,y) obtained from the three-dimensional data X(r,d,n) are two-dimensional data defined in the xy-plane. In contrast, in the fourth embodiment, the amplitude component m(x,y,z) and phase component p(x,y,z) are three-dimensional data defined in three-dimensional space.

[0084] Figure 16 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the amplitude component m(x,y,z) and phase component p(x,y,z), which are three-dimensional data, from the three-dimensional data X(r,d,n). Similar to the third embodiment shown in Figure 9, a complex signal P(r,θ,φ) is calculated from the three-dimensional data X(r,d,n). In the third embodiment (Figure 9), a two-dimensional complex signal P(x,y) is obtained from the three-dimensional complex signal P(r,θ,φ). In contrast, in the fourth embodiment, without obtaining two-dimensional data, the complex signal P(r,θ,φ) is converted into a complex signal P(x,y,z) defined in xyz orthogonal coordinates. Then, the amplitude component m(x,y,z) and phase component p(x,y,z) are calculated based on the complex signal P(x,y,z).

[0085] By calculating the amplitude component m(x,y,z) and phase component p(x,y,z) for multiple chirp frames, the amplitude component m(x,y,z) and phase component p(x,y,z) are calculated for each discretization time t1, t2, t3, ...

[0086] In step S4 (Figure 10), a biological identification index is calculated for each position in the xyz three-dimensional space. Then, in step SB5 (Figure 10), the position of the living organism is identified in the three-dimensional space and the number of living organisms is counted. At this time, in each step from step SB51 to step SB54 (Figure 11), the two-dimensional space targeted in the third embodiment can be extended to a three-dimensional space and the same calculations can be performed.

[0087] Next, we will describe the excellent effects of the fourth embodiment. In the fourth embodiment, the location and number of living organisms can be identified in three-dimensional space.

[0088] [Fifth Example] Next, the biological detection device according to the fifth embodiment will be described with reference to Figures 17 and 18. Hereinafter, the description of components common to the biological detection device according to the third embodiment, which was described with reference to Figures 9 to 15, will be omitted. In the third embodiment, a biological detection index I defined by equation (3) is used to distinguish between living organisms and stationary objects, and living organisms are extracted. In the fifth embodiment, a biological detection index I' different from biological detection index I is used to extract living organisms. Furthermore, a complex signal P' originating from a living organism (a complex identifier originating from a living organism) is calculated from the biological detection index I'. The procedure for calculating the biological detection index I' and the complex signal P' will be described below.

[0089] Figure 17 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the biological complex signal P'(r,θ,φ) from three-dimensional data X(r,d,n). First, the processing unit 30 obtains two-dimensional data X'(r,n) defined in a two-dimensional space of distance r and channel n by averaging the three-dimensional data X in the velocity direction (d direction). This procedure is the same as the procedure in the first embodiment (Figure 3).

[0090] Next, the two-dimensional data X'(r,n) is duplicated in the velocity direction (d direction) to calculate the three-dimensional data X'(r,d,n). The number of data points in the velocity direction is the same as the number of data points in the velocity direction in the three-dimensional data X(r,d,n).

[0091] Next, the biological detection index I'(r,d,n) is calculated by subtracting the three-dimensional data X'(r,d,n), which has been averaged in the velocity direction, from the original three-dimensional data X(r,d,n). Since the biological detection index I'(r,d,n) is obtained by subtracting the average velocity from each element of the three-dimensional data X(r,d,n), information caused by stationary objects that do not have velocity is removed, and information caused by living organisms, such as respiration, heartbeat, and body movement, which cause changes in the distance from the biological detection device 55 (Figure 6A) to the living organism's surface, remains. Next, the biological detection index I' is used to calculate the biologically-derived complex signal P'(r,θ,φ).

[0092] Next, referring to Figure 18, we will explain the procedure for calculating the biologically-derived complex signal P' from the biological judgment index I'.

[0093] Figure 18 is a schematic diagram showing the arrangement of various pieces of information (data) that appear in the procedure for obtaining the biologically-derived complex signal P'(r,θ,φ) from the biological judgment index I'(r,d,n).

[0094] From the biometric index I'(r,d,n), extract the i-th element in the r direction and the j-th element in the d direction. This will result in a one-dimensional biometric index I'(r i d j ,n) is obtained. Biological judgment index I' (r i d j From the complex conjugate transpose matrix I' (n) H (r i d j Find n). H (r i d j ,n) and the biological judgment index I'(r i d j From the matrix product with n), the correlation matrix R XX Calculate (n,n).

[0095] Next, by performing similar calculations for all elements in the r direction, we obtain an n×n×r three-dimensional correlation matrix R for each element in the d direction. XX We find (n,n,r). This gives us d three-dimensional correlation matrices R XX (n,n,r) is obtained. d three-dimensional correlation matrices R XX By averaging (n,n,r) in the d direction, a three-dimensional correlation matrix R is obtained. XX Find (n,n,r). Correlation matrix R XX By performing angle estimation processing using (n,n,r), we obtain the biologically-derived complex signal P'(r,θ,φ).

[0096] Next, the biologically-derived complex signal P'(r,θ,φ) is transformed into an xyz orthogonal coordinate system to obtain the biologically-derived complex signal P'(x,y,z). Then, similar to step SB1 (Figure 11), a point cloud consisting of the point where the biologically-derived complex signal P'(x,y,z) shows a local peak, and multiple points surrounding it, is extracted. Next, the point cloud distributed in three dimensions is viewed from the z direction to transform it into a point cloud distributed in the xy plane.

[0097] Next, the location and number of living organisms are determined by performing the same procedure as in steps SB52 to SB54 in Figure 11.

[0098] Alternatively, before converting a point cloud distributed in three dimensions into a point cloud distributed in the xy plane, clustering can be performed on the point cloud in three-dimensional space, and then viewed from the z direction to convert it into a point cloud and clusters distributed in the xy plane.

[0099] Next, we will describe the excellent effects of the fifth embodiment. In the third embodiment (Figure 9), the biological detection index I is calculated using equations (1), (2), and (3) based on the amplitude component m(x,y) and phase component p(x,y) of the complex signal P(x,y), and signals from stationary objects other than living organisms are removed. In contrast, as in the fifth embodiment, it is also possible to remove signals from stationary objects by subtracting the three-dimensional data X'(r,d,n), which is averaged in the velocity direction, from the three-dimensional data X(r,d,n). By removing signals from stationary objects, the position and number of living organisms can be determined.

[0100] The embodiments described above are illustrative, and it goes without saying that partial substitution or combination of the configurations shown in different embodiments is possible. Similar effects and benefits from similar configurations in multiple embodiments will not be mentioned sequentially for each embodiment. Furthermore, the present invention is not limited to the embodiments described above. For example, it will be obvious to those skilled in the art that various modifications, improvements, and combinations are possible. [Explanation of symbols]

[0101] 20 Transmitter / Receiver Unit 21 Signal Processing Unit 22 Signal generation unit 23 AD converters 24 Mixer 30 Processing Unit 40 point cloud 41P: Point indicating the peak 41A surrounding points 45A, 45B, 45C cluster 50 Observation range 51 Object 52 Stationary objects 52A Region corresponding to a stationary object 53 People 53A Area corresponding to a person 54 Wall 54A Area corresponding to the wall 55 Biological detection device D1, D2, D3, D4, Da, Db, Dc, Di - Divided sections Rx receiving antenna Tx Transmitter Antenna

Claims

1. A transmitting and receiving unit that receives reflected waves of radio waves transmitted from at least one transmitting antenna to an observation range using multiple receiving antennas, and generates an intermediate frequency signal based on the transmitted signal and the received signal, A processing unit that identifies the location of living organisms within the observation range and counts the number of living organisms based on the aforementioned intermediate frequency signal. Equipped with, The aforementioned processing unit, Based on the aforementioned intermediate frequency signal, a complex signal is calculated for each position within the observation range that reflects the presence or absence of an object and the velocity of the object. Based on the complex signal, for each position within the observation range, a biological determination index is calculated, the size of which differs depending on whether the detected object is a living organism or not, and the size of which depends on the number of living organisms. Using the aforementioned biological identification index, the location of living organisms within the observation range is identified, and the number of living organisms is counted. The aforementioned processing unit, When identifying the location of living organisms within the observation range and counting the number of living organisms, the location of living organisms is identified based on the distribution of the living organism determination index for each location within the observation range. The sum of the biological determination indices in at least a portion of the area within the observation range is calculated. Based on the sum of the aforementioned biological assessment indicators, the number of living organisms is counted. The processing unit is a biological detection device that uses an index as the biological determination index that includes the strength of the correlation between the time change of the amplitude component of the complex signal and the time change of the phase component of the complex signal for each position within the observation range.

2. The aforementioned processing unit, The aforementioned observation area is divided into multiple sections, The biological detection device according to claim 1, wherein, when correcting the number of biological organisms, the sum of the biological identification indices is calculated for each of the plurality of sections, and the number of biological organisms is counted based on the sum of the biological identification indices as well.

3. A transmitting and receiving unit that receives reflected waves of radio waves transmitted from at least one transmitting antenna to an observation range using multiple receiving antennas, and generates an intermediate frequency signal based on the transmitted signal and the received signal, A processing unit that identifies the location of living organisms within the observation range and counts the number of living organisms based on the aforementioned intermediate frequency signal. Equipped with, The aforementioned processing unit, Based on the aforementioned intermediate frequency signal, a complex signal is calculated for each position within the observation range that reflects the presence or absence of an object and the velocity of the object. Based on the complex signal, for each position within the observation range, a biological determination index is calculated, the size of which differs depending on whether the detected object is a living organism or not, and the size of which depends on the number of living organisms. Using the aforementioned biological identification index, the location of living organisms within the observation range is identified, and the number of living organisms is counted. The aforementioned processing unit, In the process of identifying the location of living organisms within the aforementioned observation range and counting the number of living organisms, From within the observation range, extract a point consisting of a point where either the absolute value of the complex signal or the biometric indicator shows a local peak, and a point cloud consisting of multiple points around the point showing the local peak. Based on the extracted point cloud, the location of the organism is identified, and the number of organisms is counted. The processing unit is a biological detection device that uses an index as the biological determination index that includes the strength of the correlation between the time change of the amplitude component of the complex signal and the time change of the phase component of the complex signal for each position within the observation range.

4. The biological detection device according to claim 3, wherein the processing unit performs clustering on the extracted point cloud to classify the point cloud into one or more clusters containing multiple points, identifies the location of a biological organism based on the cluster, and counts the number of biological organisms.

5. The biological detection device according to claim 4, wherein the processing unit determines the number of biological organisms corresponding to the cluster based on the respective feature quantities of the cluster.

6. The biological detection device according to claim 4 or 5, wherein the processing unit determines the position of a living organism based on the distribution of a plurality of points included in each of the clusters.

7. The transmitted signal is a frequency continuous modulation wave in which chirps, in which the frequency changes linearly with respect to time, repeatedly appear. The biological detection device according to any one of claims 1 to 5, wherein the processing unit performs a distance FFT for each chirp, performs a velocity FFT on the result of the distance FFT for each chirp frame containing multiple chirps, and calculates the complex signal for each position within the observation range.

8. The transmitted signal is a frequency continuous modulation wave in which chirps, in which the frequency changes linearly with respect to time, repeatedly appear. The biological detection device according to claim 6, wherein the processing unit performs a distance FFT for each chirp, performs a velocity FFT on the result of the distance FFT for each chirp frame containing multiple chirps, and calculates the complex signal for each position within the observation range.

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