Living body detection device

Abstract

According to the present invention, a position detection unit transmits radio waves into an observation range, receives reflected waves from an object, and identifies the position of the object in the observation range on the basis of the transmission signal and the reception signal. A calculation unit calculates, for each of positions within the observation range and at fixed time intervals, determination basic information at the present time point that is obtained from the transmission signal and the reception signal, and determination secondary information that is determined from the determination basic information. A living body determination unit determines, on the basis of the determination secondary information calculated by the calculation unit, whether or not the object present at the position where the presence of the object has been determined by the position detection unit is a living body. A change caused by displacement of the surface of the living body due to the respiration or heartbeat of the living body is reflected in a temporal change of the determination basic information. The calculation unit calculates the determination secondary information at the current time point on the basis of the determination basic information at the current time point and the determination secondary information calculated at an immediately preceding time.

Description

Biological detection device

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

[0002] The introduction of biodetection devices that use radar to detect living organisms such as the human body is progressing (Patent Document 1). The technology described in Patent Document 1 utilizes the property that the phase of reflected waves from living organisms shows periodic changes associated with respiration and heartbeat, while the phase of reflected waves from stationary objects other than living organisms shows a random time-series waveform due to system noise. Multiple sample data from the present to the past are used to determine whether or not the phase of the reflected wave shows periodic changes associated with respiration and heartbeat.

[0003] International Publication No. 2024 / 190105

[0004] Conventional technologies require storing multiple sample data points from the present to the past in order to determine whether the phase of the reflected wave shows periodic changes associated with respiration and heartbeat. Therefore, a large memory area is required to store these multiple sample data points. The objective of the present invention is to provide a biodetection device that can reduce the memory area required.

[0005] According to one aspect of the present invention, a biological detection device is provided comprising: a position detection unit that transmits radio waves within an observation range, receives reflected waves from an object, and identifies the position of an object within the observation range based on the transmitted signal and the received signal; a calculation unit that calculates, for each position within the observation range, current basic determination information obtained from the transmitted signal and the received signal, and secondary determination information obtained from the basic determination information, at a fixed time step width; and a biological determination unit that determines whether an object located at a position where the position detection unit has determined that an object is present is a living organism, based on the secondary determination information calculated by the calculation unit, wherein the time change of the basic determination information reflects changes caused by displacement of the surface of a living organism due to respiration or heartbeat, and the calculation unit calculates the current secondary determination information based on the current basic determination information and the secondary determination information calculated at the immediately preceding time.

[0006] To determine whether an object is a living organism, the system uses current basic judgment information and secondary judgment information from the previous time, without using prior basic judgment information. Therefore, it is not necessary to store a large amount of basic judgment information going back in time from the present. This makes it possible to reduce the amount of memory required.

[0007] Figure 1 is a block diagram of the biological detection device according to this embodiment. Figure 2 is a flowchart showing the processing procedures performed by the position detection unit 20, the calculation unit 30, and the biological determination unit 35. Figure 3 shows the determination index I obtained from the actual measurement results. ｓ This is a graph showing the magnitude. Figures 4A and 4B show the degree of variation of amplitude information m ｓ The square of and the degree of agreement c ｓ This is a graph showing the time change of the product of the two factors. Figure 5 is a flowchart showing the procedure of the biological detection method according to the comparative example. Figure 6 is a schematic diagram visually showing the number of pieces of information required to determine whether or not something is a living organism using the method according to the comparative example. Figure 7 is a schematic diagram visually showing the number of pieces of information required to determine whether or not something is a living organism using the biological detection device according to the example. Figure 8 is the judgment index I calculated by the biological detection device according to the example. ｓ Figure 9A is a schematic plan showing the arrangement of the objects in the evaluation experiment, Figure 9B is a diagram showing the distribution of radar data obtained by signal processing with a general radar device that does not have the function of distinguishing between living organisms and stationary objects, using shades of gray, and Figures 9C and 9D are diagrams showing the distribution of judgment indices I and Is calculated by the methods of the comparative example and the example, using shades of gray.

[0008] A biological detection device according to one embodiment will be described with reference to the drawings from Figure 1 to Figure 9D. Figure 1 is a block diagram of the biological detection device according to the embodiment. The biological detection device according to the embodiment includes a position detection unit 20, a calculation unit 30, and a biological determination unit 35. The position detection unit 20 detects the position (distance and angle) and velocity of the object 51 using a frequency continuous modulation (FMCW) method. Since the FMCW method is a well-known radar method, a detailed explanation will be omitted here, and a brief explanation will be given below.

[0009] The position detection unit 20 includes a signal processing unit 21, a signal generation unit 22, a plurality of AD converters 23, a plurality of mixers 24, a transmitting antenna Tx, and a plurality of receiving antennas Rx.

[0010] The signal generation unit 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. The position detection unit 20 determines the position of the objects 51 within the observation range 50 based on the transmitted and received signals.

[0011] More specifically, a mixer 24, prepared for each of the multiple receiving antennas Rx, mixes the transmitted signal and the received signal to generate an intermediate frequency signal (IF signal). An AD converter 23 performs AD conversion on each of the IF signals. A signal processing unit 21 performs signal processing on the IF signals to obtain the position information (distance information and angle information) and velocity information of the object 51.

[0012] The calculation unit 30 calculates current basic determination information and secondary determination information based on the basic determination information for determining whether the object 51 is a living organism, at a fixed time step interval. The secondary determination information is calculated from current and past basic determination information. Specifically, the calculation unit 30 calculates current secondary determination information based on current basic determination information and secondary determination information calculated at the previous time. The living organism determination unit 35 determines whether the object 51 detected by the position detection unit 20 is a living organism based on the secondary determination information calculated by the calculation unit 30.

[0013] Next, with reference to Figure 2, the detailed processing of the position detection unit 20, the calculation unit 30, and the biological determination unit 35 will be described. Figure 2 is a flowchart showing the processing procedures performed by the position detection unit 20, the calculation unit 30, and the biological determination unit 35.

[0014] First, the calculation unit 30 sets initial values ​​for the secondary determination information (step S1). Details of the secondary determination information will be explained later. Next, the position detection unit 20 transmits radio waves and receives reflected waves from the object 51 (Figure 1) (step S2). Distance FFT is performed on each chirp for the IF signals obtained from the transmitted and received signals (step S3). Furthermore, for each chirp frame consisting of multiple chirps, velocity FFT is performed on the multiple complex signals after the distance FFT processing (step S4).

[0015] Subsequently, based on the distance and velocity information obtained from distance FFT processing and velocity FFT processing performed on the IF signals corresponding to each of the multiple receiving antennas Rx, angle estimation processing is performed to calculate complex radar data for each position specified by distance and angle (step S5). The radar data includes information on the magnitude and phase of the reflected waves from each position.

[0016] The procedure from step S2 to step S5 is the same as the procedure used in a typical FMCW radar system.

[0017] The radar data for a discretized position at time n, distance r, and angle θ, is denoted as P(r,θ,n). Time is discretized in units of chirp frames, which consist of multiple chirps that are the units for performing the velocity FFT. The radar data P(r,θ,n) is a complex number and includes amplitude information m(r,θ,n) and phase information p(r,θ,n). The amplitude information m is the absolute value of the radar data P, and the phase information p is the arctangent (arctan) of the value obtained by dividing the imaginary part of the radar data P by the real part. Since noise is superimposed on the radar data P at the position where an object is detected, noise is also superimposed on the amplitude information m and the phase information p. This noise component is so-called random noise, and there is no correlation between the noise component that appears in the time change of the amplitude information m and the noise component that appears in the time change of the phase information p.

[0018] When the detected object is a living body, in addition to the noise component, a time change due to a body surface displacement that periodically changes according to the respiration and heartbeat of the living body is superimposed on the time changes of the amplitude information m and the phase information p. The time change caused by the body surface displacement is relatively large compared to the random noise component. Therefore, the correlation between the time change of the amplitude information m and the time change of the phase information p at the position where the living body is detected becomes stronger.

[0019] By utilizing such properties of the amplitude information m and the phase information p, it is possible to determine whether the detected object is a living body. In the embodiment, based on the current amplitude information m and phase information p, determination secondary information for determining whether the object is a living body is calculated. The amplitude information m and the phase information p that serve as the basis for calculating the determination secondary information are referred to as determination basis information.

[0020] Next, radar data corresponding to a speed of zero is extracted from the complex radar data (step S6).

[0021] Next, the determination secondary information at the current time is calculated (step S7). Hereinafter, the determination secondary information and its calculation method will be described.

[0022] The determination secondary information includes the moving average of the amplitude information m of the radar data P (hereinafter referred to as the amplitude moving average m ｗ ), the moving average of the phase information p (hereinafter referred to as the phase moving average p ｗ ), the degree of variation m ｓ representing the degree of variation of the amplitude information, and the degree of coincidence c ｓ representing the strength of the correlation between the time change of the amplitude information and the time change of the phase information. As initial values of these determination secondary information, for example, zero is set in step S1. When the current time is n, these determination secondary information at the previous time n - 1 have already been calculated and stored in the arithmetic unit 30.

[0023] The amplitude moving average m ｗ at the current time (time n) of the position (r, θ) can be calculated by the following formula. Here, γ is a real number greater than 0 and less than 1. Equation (1) means that the exponentially weighted moving average of the amplitude information m is being calculated. γ is called the smoothing coefficient.

[0024] Similarly, the phase-shifted average p of position (r, θ) at the current time (time n) ｗ This can be calculated using the following formula. Equation (2) means that the exponentially weighted moving average of the phase information p is being calculated.

[0025] Equations (1) and (2) represent the basic decision information m(r, θ, n) and p(r, θ, n) at the current time (time n), and the secondary decision information m at the previous time n-1. ｗ (r, θ, n-1), p ｗ Based on (r, θ, n-1), the current judgment secondary information m ｗ (r, θ, n), p ｗ This shows that (r, θ, n) can be calculated.

[0026] Furthermore, the calculation unit 30 calculates the degree of amplitude variation of the position (r, θ) at the current time (time n) using the following formula m ｓ Calculate. Here, β is a real number greater than 0 and less than 1. Equation (3) shows the amplitude moving average m at the current point in time. ｗ This means that the exponentially weighted moving average of the absolute values ​​of the deviations of the amplitude information m is calculated. Therefore, the degree of variability of the amplitude information m is calculated. ｓ The square of can be considered an indicator that shows a similar trend to the variance of amplitude information m. That is, as the variance of amplitude information m increases, the degree of variability m ｓ It also grows in a similar manner.

[0027] Equation (3) is given by amplitude information m(r, θ, n), which is the basic judgment information at the current time, and amplitude moving average m, which is the secondary judgment information at the current time. ｗ (r, θ, n), and the degree of variation m, which is secondary judgment information at the previous time n-1. ｓ Based on (r, θ, n-1), the current degree of variation m ｓ This means that (r, θ, n) is calculated.

[0028] Next, the calculation unit 30 calculates the degree of agreement c of the position (r, θ) at the current time (time n) using the following formula. ｓ Calculate (r, θ, n). Here, α is a real number greater than 0 and less than 1, and the parameter c(r, θ, n) is defined by the following equation. Equation (4) means that the weighted moving average of the parameter c, weighted by α, is calculated. Therefore, the degree of agreement c ｓ Similar to covariance, the value of tends to increase as the correlation between the time evolution of amplitude information m and the time evolution of phase information p becomes stronger.

[0029] Equations (4) and (5) represent the amplitude information m(r, θ, n) and phase information p(r, θ, n), which are the basic judgment information at the current time, and the amplitude moving average m, which is the secondary judgment information at the current time. ｗ (r, θ, n), phase shift information p ｗ (r, θ, n), and the degree of agreement c, which is secondary judgment information at the previous time n-1. ｓ Based on (r, θ, n-1), the current degree of agreement c ｓ This means that (r, θ, n) is calculated.

[0030] As described above, the calculation unit 30 uses the current basic judgment information (amplitude information m, phase information p) and the secondary judgment information (amplitude moving average m) calculated at the previous time. ｗ , phase moving average p ｗ , degree of variation m ｓ , degree of agreement c ｓ Based on this, the current secondary judgment information is calculated.

[0031] The calculation unit 30 calculates the secondary decision information and then stores the current secondary decision information (step S8). The stored secondary decision information is used to calculate the secondary decision information at the next time. For example, the secondary decision information at the previous time n-1 is overwritten with the newly calculated secondary decision information at the current time.

[0032] Once the current secondary judgment information is calculated, the biological determination unit 35 determines whether the object is a living organism or not based on the secondary judgment information (step S9). The procedure from step S2 is then repeated. As an example, the biological determination unit 35 determines the judgment index I defined by the following formula for each position (r, θ). ｓ The determination is made based on (r, θ, n).

[0033] The magnitude of radar data P serves as an indicator for determining whether or not an object exists at that location. The degree of variation in amplitude information m ｓ The square of this value is an index used to determine whether or not a time-varying amplitude of a certain magnitude is present, rather than random noise. The degree of agreement is c. ｓ This is an indicator used to determine whether an object at a given location is a living organism, as it exhibits a strong correlation between the time evolution of amplitude information m and phase information p.

[0034] Figure 3 shows the judgment index I obtained from the actual measurement results. ｓ This graph shows the magnitude of [the variable]. The horizontal axis represents time in units of [seconds], and the vertical axis represents the judgment index I. ｓ This is expressed in units of [dB]. The white triangle symbol in Figure 3 represents the biologically-based judgment index I. ｓ The black triangle symbol indicates a judgment index I based on stationary objects other than living organisms (hereinafter sometimes simply referred to as stationary objects). ｓ This indicates.

[0035] Biologically-based judgment index I ｓ This is greater than the judgment index Is based on a stationary object, and the values ​​of the two are clearly distinct. In the example shown in Figure 3, for example, if the judgment threshold is set to 160 dB, the judgment index I ｓ Based on this, it is possible to determine whether the detected object is a living organism or not.

[0036] Next, the values ​​of the smoothing coefficients α, β, and γ will be explained with reference to Figures 4A and 4B. The smoothing coefficients α, β, and γ are selected from the range greater than 0 and less than 1. Based on the values ​​of the smoothing coefficients α, β, and γ, the biological judgment index I is determined. ｓ Judgment index I based on stationary objects ｓThe difference between the two increases or decreases. To improve the accuracy of biological diagnosis, it is best to select smoothing coefficients α, β, and γ such that the difference between the two increases.

[0037] Figures 4A and 4B show the degree of variation in amplitude information m ｓ The square of and the degree of agreement c ｓ This graph shows an example of the time variation of the product of [the two factors]. The horizontal axis represents time in units of [seconds], and the vertical axis represents the degree of variation of the amplitude information m ｓ The square of and the degree of agreement c ｓ The product of these values ​​is expressed in units [dB]. Figure 4A shows the calculation results when α = β = 0.1 and γ = 0.9, and Figure 4B shows the calculation results when α = β = 0.9 and γ = 0.1. Based on biological conditions ｓ ×m ｓ ２ c based on a stationary object ｓ ×m ｓ ２ This means that the example shown in Figure 4A is more clearly separated than the example shown in Figure 4B. The values ​​of the smoothing coefficients α, β, and γ were determined by conducting various evaluation experiments and based on biological data. ｓ ×m ｓ ２ c based on a stationary object ｓ ×m ｓ ２ It is best to set the values ​​appropriately so that the two are more clearly separated.

[0038] Next, referring to Figures 5 to 9D, the superior effects of this embodiment will be explained in comparison with the comparative example.

[0039] Figure 5 is a flowchart showing the procedure for the biological detection method according to the comparative example. The steps from step S2 to step S6 are the same as the steps from step S2 to step S6 (Figure 2) performed by the biological detection device according to the embodiment. In the comparative example, the amplitude and phase of the radar data are stored for each position defined by distance and angle (step SA7).

[0040] The procedure from step S2 to step SA7 is repeated until N frames of chirp frames, which are the units for performing the speed FFT, are stored (step SA8). Once N frames of signals have been stored, the signal of the oldest frame is deleted (step SA9).

[0041] Next, secondary decision information is calculated from the N frames of signal (step SA10). The secondary decision information includes the degree of agreement c between the amplitude information m and phase information p for a total of N frames from the present and past. ｃ and the variance s of amplitude information m ２ This includes the degree of agreement between amplitude information m and phase information p, c. ｃ It is defined by the following formula: Here, m ａ (r, θ, n) is the average of the amplitude information m for the total of N frames, including the current and past frames, and p ａ (r, θ, n) is the average of the phase information p for the total of N frames, including current and past frames. As can be seen from equation (7), the degree of agreement c ｃ To calculate this, we need information from the current time (time n) and information from the past N-1 frames, from time n-1 to time n-N+1.

[0042] Variance s of amplitude information m ２ It is defined by the following formula. As can be seen from equation (8), the variance s of the amplitude information m ２ To calculate this, we need information from the current time (time n) and information from the past N-1 frames, from time n-1 to time n-N+1.

[0043] The judgment index I is defined by the following formula. Matching degree C ｃ and the variance s of amplitude information m ２ However, the degree of agreement c in equation (6) ｓ and the degree of variation of amplitude information m ｓ This corresponds to the square of [the specified value]. Based on this judgment index I, a determination is made as in the above embodiment as to whether the detected object is a living organism or not (step SA11).

[0044] Figure 6 is a schematic diagram visually showing the number of pieces of information required to determine whether or not something is a living organism using the method described in the comparative example. Each position, defined by distance information r and angle information θ, is represented by a single box (sometimes called a bin). For each of the multiple boxes, radar data P, represented as a complex number, is defined. From the radar data P, amplitude information m and phase information p are calculated. At a specific time, the multiple boxes are arranged in a two-dimensional manner so as to fill the observation range 50 (Figure 1). As the multiple boxes arranged in the two-dimensional manner are aligned along the time axis, the multiple boxes are arranged in a three-dimensional manner.

[0045] Focusing on a specific position (r, θ), N radar data points P are defined, going back from time n to time n-N+1. From these N radar data points, N amplitude information points m and N phase information points p are calculated. From the N amplitude information points m and phase information points p, a judgment index I(r, θ, n) is calculated.

[0046] Therefore, for each location within the observation range 50 (Figure 1), the current radar data and the (N-1) past radar data must be stored.

[0047] Figure 7 is a schematic diagram visually showing the number of pieces of information necessary to determine whether or not something is a living organism using the biological detection device according to the above embodiment. In the above embodiment, boxes defining the radar data P at the current time (time n) are arranged in two dimensions so as to fill the observation range 50. Furthermore, the amplitude moving average m at the previous time n-1 is also included. ｗ (n-1), phase moving average p ｗ (n-1), degree of variation m ｓ (n-1), degree of agreement c ｓ Boxes that define (n-1) are arranged in two dimensions in the direction of distance r and angle θ.

[0048] The calculation unit 30 determines the current judgment index I at position (r, θ) ｓ The calculation of (r, θ, n) involves amplitude information m(r, θ, n) obtained from the current radar data P, phase information p(r, θ, n), and the amplitude moving average m at the previous time n-1. ｗ (r, θ, n-1), phase moving average p ｗ(r, θ, n-1), degree of variation m ｓ (r, θ, n-1), degree of coincidence c ｓ We use (r, θ, n-1). This information prior to time n-2 is unnecessary.

[0049] Next, the judgment indicators I and I in the above embodiment and comparative example are ｓ The memory capacity required to calculate this will be explained. Let R be the number of boxes arranged in the distance r direction, A be the number of boxes arranged in the angle θ direction, and N be the number of boxes arranged in the time t direction in Figure 6. If the total number of boxes when adopting the methods of the comparative example and the example is denoted as Bc and Be, respectively, then the ratio Be / Bc is expressed by the following formula.

[0050] In the comparative example, the real and imaginary values ​​of the radar data P are defined in one box, while in the embodiment, the real number (i.e., only the real part) is defined in one box. As an example, assuming that 2 bytes of memory are required to store the real and imaginary parts, 4 bytes of memory are required per box in the comparative example, while 2 bytes of memory are required per box in the embodiment. If the memory capacities required when adopting the methods of the comparative example and the embodiment are denoted as Mc and Me, respectively, the ratio Me / Mc between the two can be expressed by the following formula.

[0051] In the comparative example, the sample size N used to calculate the moving average in equations (7) and (8) is generally much larger than 2. Therefore, adopting the method according to the example reduces the amount of memory required to store the information necessary for the calculation compared to adopting the method according to the comparative example.

[0052] Figure 8 shows the judgment index I calculated by the biodetector according to the embodiment. ｓ This graph shows the judgment index I calculated using the comparative example method. The horizontal axis represents time in units of [seconds], and the vertical axis represents the judgment index I. ｓ I is expressed in units of [dB]. The white triangle symbol and the black triangle symbol represent the biologically-based judgment index I, respectively. ｓ and judgment index I based on stationary objects ｓ This shows the judgment index I shown in Figure 3. ｓThis is identical. The white and black circles indicate the judgment index I based on living organisms and judgment index I based on stationary objects, respectively, calculated using the method described in the comparative example.

[0053] In both the method according to the examples and the method according to the comparative examples, the judgment index based on living organisms and the judgment index based on stationary objects are clearly separated. The degree of separation between the two is the same in the examples and the comparative examples. Judgment index I calculated by the method according to the examples with reduced memory capacity. ｓ It was confirmed that, using this method, it is possible to distinguish between living organisms and stationary objects, similar to the case of the comparative example.

[0054] Next, the results of an evaluation experiment to detect living organisms will be explained with reference to Figures 9A to 9D. Figure 9A is a schematic plan view showing the arrangement of the objects in the evaluation experiment. An FMCW radar device 55 is positioned approximately in the center of the shorter side of the rectangular observation range 50. A stationary object 52 is positioned within the observation range 50, and a person 53 is located further away from the stationary object 52.

[0055] Figure 9B shows the distribution of radar data obtained by signal processing using a general radar device that does not have the function to distinguish between living organisms and stationary objects, indicated by shades of gray. The darker the gray area, the larger the radar data. A region 52A with a large amount of radar data appears corresponding to the stationary object 52, and a region 53A with a moderately large amount of radar data appears corresponding to the person 53.

[0056] Figures 9C and 9D show the distribution of judgment indices I and Is calculated by the comparative example and embodiment, respectively, using shades of gray. In both cases, it can be seen that the radar data corresponding to the stationary object 52 is suppressed, and the region 53A with large radar data corresponding to the person 53 is emphasized. As shown in Figures 9C and 9D, the comparative example and embodiment can distinguish between the stationary object 52 and the person 53, and detect only the person 53. Furthermore, in the embodiment, it was confirmed that the stationary object 52 and the person 53 can be distinguished with the same level of accuracy as the comparative example, despite the reduced memory capacity used compared to the comparative example.

[0057] Next, a modification of the above embodiment will be described. In the above embodiment, as shown in formulas (1) and (2), the amplitude moving average m ｗ and the phase moving average p ｗ are exponential weighted moving averages, but other moving averages may be employed. For example, the amplitude moving average and the phase moving average may be calculated such that the weights of the amplitude information and the phase information become lighter as going back in the past on the time axis.

[0058] In the above embodiment, as the determination basis information used to determine whether the detected object is a living body, the amplitude information m and the phase information p of the radar data P in which the periodic displacement of the body surface due to the respiration and heartbeat of the living body is reflected as a time change are used, but other information in which the periodic displacement of the body surface is reflected in the time change may be adopted as the determination basis information.

[0059] In the above embodiment, as the determination index I ｓ for determining whether the object is a living body, as shown in formula (6), the size of the radar data P, the degree of coincidence c ｓ , and the degree of variation m ｓ are used for calculation, but as the determination index I ｓ , without using the radar data P, the degree of coincidence c ｓ and the degree of variation m ｓ may be used. As an example, as shown in FIG. 4A, based on the product of the degree of coincidence c ｓ and the square of the degree of variation m ｓ , it is possible to determine whether the object is a living body.

[0060] Furthermore, the reason for using the degree of variation m ｓ in the determination index I ｓ is that attention is paid to the fact that the degree of variation of the amplitude due to the displacement of the body surface of the living body is larger than the degree of variation of the amplitude of the random noise. The degree of variation m ｓ is not essential for determining whether the object is a living body. For example, only the degree of coincidence c ｓ may be adopted as the determination index I ｓ .

[0061] 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, etc. may be used. In this case, among the various information obtained from the received signal, information that reflects the periodic displacement of the body surface caused by the living organism's respiration and heartbeat may be used as the basic information for determination.

[0062] The above-described embodiments and modifications are illustrative, and it goes without saying that partial substitution or combination of the configurations shown in the embodiments and modifications is possible. Similar effects and benefits due to similar configurations in the embodiments and modifications will not be mentioned sequentially for each embodiment and modification. 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 changes, improvements, and combinations are possible.

[0063] 20 Position detection unit 21 Signal processing unit 22 Signal generation unit 23 AD converter 24 Mixer 30 Calculation unit 35 Biological detection unit 50 Observation range 51 Object 52 Stationary object 52A Area where an object was detected 53 Person 53A Area where a person was detected 55 Radar device Rx Receiving antenna Tx Transmitting antenna

Claims

1. A biological detection device comprising: a position detection unit that transmits radio waves within an observation range, receives reflected waves from an object, and identifies the position of an object within the observation range based on the transmitted signal and the received signal; a calculation unit that calculates, for each position within the observation range, current basic determination information obtained from the transmitted signal and the received signal, and secondary determination information obtained from the basic determination information, at a fixed time step width; and a biological determination unit that determines whether an object located at a position where the position detection unit has determined that an object is present is a living organism, based on the secondary determination information calculated by the calculation unit, wherein the time change of the basic determination information reflects changes caused by displacement of the surface of a living organism due to respiration or heartbeat, and the calculation unit calculates the current secondary determination information based on the current basic determination information and the secondary determination information calculated at the immediately preceding time.

2. The biological detection device according to claim 1, wherein the position detection unit identifies the position of an object within the observation range using a continuous frequency modulation method, the basic determination information includes amplitude information and phase information of complex radar data obtained for each position within the observation range by signal processing an intermediate frequency signal obtained by mixing the transmitted signal and the received signal, and the secondary determination information includes an amplitude moving average which is a moving average of the amplitude information, a phase moving average which is a moving average of the phase information, a degree of variation which represents the degree of variation of the amplitude information, and a degree of agreement which represents the strength of the correlation between the time change of the amplitude information and the time change of the phase information.

3. The biological detection device according to claim 2, wherein the calculation unit calculates the amplitude moving average at the current time based on the amplitude information at the current time and the amplitude moving average calculated at the previous time; calculates the phase moving average at the current time based on the phase information at the current time and the phase moving average calculated at the previous time; calculates the degree of variation at the current time based on the amplitude information at the current time, the amplitude moving average, and the degree of variation calculated at the previous time; and calculates the degree of agreement at the current time based on the deviation of the amplitude information from the amplitude moving average at the current time, the deviation of the phase information from the phase moving average at the current time, and the degree of agreement calculated at the previous time.

4. The biological detection device according to claim 2 or 3, wherein the calculation unit calculates the amplitude moving average such that the weight of the amplitude information decreases as you go further back in time on the time axis, and calculates the phase moving average such that the weight of the phase information decreases as you go further back in time on the time axis.

5. The biological detection device according to claim 4, wherein the calculation unit calculates an exponentially weighted moving average as the amplitude moving average and the phase moving average.

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