Information processing device and sensing method

Abstract

To provide an information processing device for sensing an object.SOLUTION: An information processing device for sensing an object by using an FMCW radar is provided. The information processing device includes a data processing part for processing a reception signal based on a reception wave of the FMCW radar to generate a predetermined number of bins of power spectrum signals, an acquisition part for acquiring a plurality of peak bins corresponding to the object on the basis of the power spectrum signals, an extraction part for extracting output signals corresponding to the power spectrum signals, and a correction part for correcting the phase of the output signals in accordance with the bin numbers of the plurality of peak bins. The data processing part may apply a window function of a higher order than a rectangular window to the reception signal. The correction part may correct the phase of the output signals so as to make the difference between a phase to be added or subtracted to / from the odd bin numbers and a phase to be added or subtracted to / from the even bin numbers be (2×i+1)π[rad]. i may be an arbitrary integer.SELECTED DRAWING: Figure 5A

Description

[Technical Field]

[0001] The present invention relates to an information processing device and a sensing method. [Background technology]

[0002] BACKGROUND ART Conventionally, an information processing device that senses an object using a Doppler radar is known (see, for example, Patent Document 1). Patent Document 1 Patent No. 6029108 Summary of the Invention

[0003] A first aspect of the present invention provides an information processing device that senses an object using an FMCW radar, the information processing device comprising: a data processing unit that processes a received signal based on a received wave of the FMCW radar to generate a power spectrum signal with a predetermined number of bins; an acquisition unit that acquires multiple peak bins corresponding to the object based on the power spectrum signal; an extraction unit that extracts an output signal corresponding to the power spectrum signal; and a correction unit that corrects the phase of the output signal according to bin numbers of the multiple peak bins. The data processing unit may apply a window function of a higher order than a rectangular window to the received signal. The correction unit may correct the phase of the output signal so that the difference between the phase to be added or subtracted to the odd-numbered bin numbers and the phase to be added or subtracted to the even-numbered bin numbers is (2×i+1)π [rad], where i may be any integer.

[0004] The plurality of peak bins may include a peak bin having a predetermined first bin number and a peak bin having a second bin number different from the first bin number. The correction unit may correct the phase of the peak bin having the second bin number without correcting the phase of the peak bin having the first bin number.

[0005] The data processing unit may acquire a distance power spectrum relating to the distance to the object by performing a distance FFT on the received signal, and the acquisition unit may acquire a peak bin indicating the distance to the object based on the distance power spectrum.

[0006] The correction section may correct a phase of a peak bin of the distance power spectrum.

[0007] The data processing unit may acquire a velocity power spectrum relating to the velocity of the object by performing a velocity FFT on the received signal, and the acquisition unit may acquire a peak bin indicating the velocity of the object based on the velocity power spectrum.

[0008] The acquisition unit may acquire the peak bin of the velocity FFT based on a data string corresponding to a peak bin position identified by the distance FFT.

[0009] The data processing unit may perform an angular FFT on the received signal to acquire an angular power spectrum relating to an angle with the object, and the acquisition unit may acquire a peak bin indicating an angle with the object based on the angular power spectrum.

[0010] The acquisition section may acquire the peak bin of the angle FFT based on a data string corresponding to a peak bin position identified by the distance FFT.

[0011] The information processing device may include a data conversion unit that converts a plurality of data corresponding to the object into clustered data.

[0012] The information processing device may include a tracking unit that tracks the object based on past data of the object when data corresponding to the object is not obtained within a predetermined period of time.

[0013] The extracting section may extract IQ data including an in-phase component and a quadrature component orthogonal to the in-phase component from the peak bin acquired by the acquiring section.

[0014] The data processing unit may process the received signals using a CAPON or compressed sensing algorithm.

[0015] A second aspect of the present invention provides a sensing method for sensing an object using an FMCW radar, the sensing method including: processing a received signal based on a received wave of the FMCW radar to generate a power spectrum signal with a predetermined number of bins; acquiring multiple peak bins corresponding to the object based on the power spectrum signal; extracting an output signal corresponding to the power spectrum signal; and correcting the phase of the output signal according to the bin numbers of the multiple peak bins. The generating the power spectrum signal may include applying a window function of a higher order than a rectangular window to the received signal. The correcting step may include correcting the phase of the output signal so that the difference between the phase to be added or subtracted to the odd-numbered bin numbers and the phase to be added or subtracted to the even-numbered bin numbers is (2 × i + 1)π [rad], where i may be any integer.

[0016] The above summary of the invention does not list all of the features of the present invention, and subcombinations of these features may also be inventions. [Brief explanation of the drawings]

[0017] [Figure 1A] 6 shows an overview of the configuration of a system 600. [Figure 1B] 2 shows an example of a transmission wave 220 transmitted by the transmitting unit 120. [Figure 1C] 10 is a diagram for explaining an example of signal processing by the information processing device 500. FIG. [Figure 1D] 2 is a diagram for explaining the distance R, velocity V, and angle θ of an object 210. FIG. [Figure 1E]1 is a diagram for explaining a distance R, a velocity V, an angle θ1, and an angle θ2 of an object 210. FIG. [Figure 2] FIG. 6 is a diagram for explaining the operating principle of the system 600. [Figure 3A] 1 is a diagram for explaining the principle of detecting a distance R to an object 210. FIG. [Figure 3B] 1 is a diagram for explaining the principle of detecting the velocity V of an object 210. FIG. [Figure 3C] 10 is a diagram for explaining the principle of detecting the angle θ of the object 210. FIG. [Figure 4A] 10 is a diagram for explaining a method of sensing an object 210 with body movement. FIG. [Figure 4B] An example of the effect of body movement of the object 210 is shown. [Figure 5A] 1 shows an example of the configuration of a signal processing unit 400. [Figure 5B] An example of the operation of the signal processing unit 400 will be described. [Figure 5C] 4 is a diagram for explaining the configuration of a data processing unit 410 and an acquisition unit 420 in more detail. [Figure 5D] 10 is an example of a phase correction algorithm of the correction unit 440. [Figure 5E] 10 is a modified example of the phase correction algorithm of the correction unit 440. [Figure 6A] 10 shows an example of the relationship between signal intensity and phase with respect to bin number. [Figure 6B] 10 shows an example of the relationship between signal intensity and phase with respect to bin number. [Figure 6C] 10 shows an example of the relationship between signal intensity and phase with respect to bin number. [Figure 6D] 10 shows an example of the relationship between signal intensity and phase with respect to bin number. [Figure 7A] 10 shows a modified example of the signal processing unit 400. [Figure 7B] 4 is a diagram for explaining the configuration of a data processing unit 410 and an acquisition unit 420 in more detail. [Figure 8A] 10 shows a modified example of the signal processing unit 400. [Figure 8B]An example of the operation of the signal processing unit 400 will be described. [Figure 8C] An example of data conversion by the data control unit 450 will be described. [Figure 9] 10 shows an example of a result of phase compensation performed by the information processing device 500. DETAILED DESCRIPTION OF THE INVENTION

[0018] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0019] 1A shows an outline of the configuration of a system 600. The system 600 includes a transmitting / receiving unit 100 and an information processing device 500. The transmitting / receiving unit 100 includes a transmitting unit 120 and a receiving unit 140.

[0020] The transmitter 120 transmits a frequency modulated continuous wave (FMCW) radar signal to the target 210 as a transmission wave 220. The FMCW radar is a continuous wave radar that modulates the frequency. For example, the FMCW radar has a burst wave including multiple chirps. In each chirp, the frequency is swept over time. The transmitter 120 may have multiple transmission antennas.

[0021] The receiver 140 receives the FMCW radar wave reflected by the object 210. The receiver 140 in this example demodulates the received wave 230 to generate an analog beat signal 150. The receiver 140 may have multiple receiving antennas. By providing multiple receiving antennas, information regarding the angle θ of the object 210 as viewed from the transmitter / receiver 100 can be obtained.

[0022] The beat signal 150 is an example of an IF signal down-converted to an IF (Intermediate Frequency) frequency proportional to the TOF (Time of Flight) of the reflected wave. The TOF is the time it takes for the transmitted transmission wave 220 to be received as a reflected wave, and increases as the distance R between the information processing device 500 and the target object 210 increases. The frequency of the beat signal 150 is proportional to the TOF, and therefore also changes in proportion to the distance R.

[0023] The transmission / reception control unit 160 controls the transmission and reception of signals by the transmission unit 120 and the reception unit 140. In one example, the transmission / reception control unit 160 controls the modulation width and period of the chirp frequency of the transmission wave 220.

[0024] The information processing device 500 performs AD conversion on the beat signal 150 and performs signal processing to calculate the distance R and velocity V of the object 210. The information processing device 500 of this example can detect minute vibrations of several mm by calculating the distance R using the phase. The information processing device 500 has an input unit 300 and a signal processing unit 400.

[0025] The input section 300 converts the analog beat signal 150 input from the receiving section 140 into a digital received signal. The input section 300 may be an ADC configured by an integrated circuit such as an RFIC.

[0026] The signal processing unit 400 is a digital signal processor (DSP) that performs signal processing such as FFT based on the digital received signal output by the input unit 300. The signal processing unit 400 detects the object 210 by processing the digital received signal. In this specification, detecting the object 210 refers to acquiring the distance R, velocity V, angle θ, etc. of the object 210 and detecting the presence of the object 210. The distance R, velocity V, and angle θ of the object 210 will be described later.

[0027] Furthermore, the signal processing unit 400 senses the object 210 based on the micro-vibration data of the object 210. In this specification, sensing the object 210 refers to acquiring biosignals such as micro-vibration data of the object 210. Biosignals exist when the object 210 is a living organism, and are generated by breathing, heartbeat, and the like.

[0028] The micro-vibration data is data based on the heartbeat and breathing of the object 210. In one example, the information processing device 500 can obtain, as the micro-vibration data, a resolution with the wavelength of the FMCW radar as the maximum. For example, the information processing device 500 can obtain a resolution 100 to 1000 times that of one wavelength of the millimeter wave band (a frequency band of about 30 to 300 GHz) used in the FMCW radar.

[0029] The system 600 senses the object 210 by transmitting an FMCW radar to the object 210. The system 600 can sense the object 210 even when the relative velocity between the system 600 and the object 210 is zero by appropriately processing the received signal based on the modulated frequency of the FMCW radar.

[0030] FIG. 1B shows an example of a transmission wave 220 transmitted by the transmitter 120. The transmission wave 220 includes m chirps in one burst, where m is an integer equal to or greater than 2. The transmitter / receiver 100 modulates the frequency of the chirps and calculates the distance R, velocity V, and angle θ of the object 210 by analyzing the difference between the transmission wave 220 and the reception wave 230. The transmitter / receiver 100 may adjust the modulation width or period of the chirp frequency as appropriate depending on the position of the object 210, etc. The transmission wave 220 in this example includes m chirps of the same waveform, but may also include chirps of different waveforms.

[0031] FMCW radar is a radar that detects the distance to a target and its relative speed by utilizing the time difference between the return echoes from the target object 210. For example, FMCW radar linearly increases and decreases the frequency in a cycle of several microseconds to several hundred microseconds, and uses only either the up or down frequency for detection. However, in the FMCW system, both the up and down frequencies may be used for detection.

[0032] FMCW radar can simultaneously detect angle information by arranging multiple channels. For example, FMCW radar can achieve long-distance detection in the 76G band (76-77 GHz) and medium- or short-distance detection in the 79G band (77-81 GHz). Note that FMCW radar may also use a method that linearly raises and lowers the frequency in cycles ranging from several milliseconds to several hundred milliseconds.

[0033] FIG. 1C is a diagram for explaining an example of signal processing by information processing device 500. As shown in FIG.

[0034] The chirp frequency of the transmitted wave 220 increases linearly from a low frequency during the period from time T0 to time Tm. The received wave 230 is received after a delay time Td depending on the distance R to the object 210. The delay time Td varies depending on the distance R to the object 210.

[0035] The beat signal 150 is generated in the transmitting / receiving unit 100. The beat signal 150 is generated from the difference between the chirp of the transmitted wave 220 and the chirp of the received wave 230. The frequency of the beat signal 150 is proportional to the magnitude of the delay time Td.

[0036] The spectrum data is calculated by the signal processing unit 400 using the signal between time Td and time Tm when the frequency of the beat signal 150 is relatively stable. A specific method for calculating the spectrum data will be described later.

[0037] 1D is a diagram for explaining the distance R, velocity V, and angle θ of the object 210. In this example, for simplicity, the transmitting unit 120 and the receiving unit 140 are considered to be in the same position.

[0038] The object 210 is moving at a speed V at a position at a distance R from the transceiver unit 100. The speed V is the relative speed between the transceiver unit 100 and the object 210. The angle θ is the angle of the object 210 as seen from the transceiver unit 100. Specifically, if the direction in which the receivers 140 are arranged is the X-axis direction and the direction in which the FMCW radar is emitted is the Y-axis, the angle θ is the angle between the Y-axis and the position of the object 210 on the XY plane.

[0039] FIG. 1E is a diagram for explaining the distance R, velocity V, angle θ1, and angle θ2 of the object 210. The information processing device 500 can detect and sense the object 210 using a similar principle as a so-called 3D radar that detects a new axis (Z axis) perpendicular to the XY plane. In this case, the information processing device 500 acquires three-dimensional information by using the angle θ2 projected onto the YZ plane in addition to the angle θ1 projected onto the XY plane. Note that in this specification, when the angle θ is simply referred to, it may be interpreted as including both the angle θ1 and the angle θ2.

[0040] 2 is a diagram illustrating the operating principle of the system 600. The system 600 uses a data cube 38 to obtain information about the distance R, velocity V, and angle θ of the object 210.

[0041] The transceiver 100 has multiple channels. In one example, the transceiver 100 has one transmitting antenna and k receiving antennas, where k is an integer greater than or equal to 1. The transceiver 100 has multiple channels, which allows it to detect the angle θ. Reflected signals from the object 210a and the object 210b are input to the k receiving antennas, respectively.

[0042] The data cube 38 includes data strings for the distance R, the velocity V, and the angle θ. The data cube 38 includes a distance data string obtained by the distance FFT, a velocity data string obtained by the velocity FFT, and an angle data string obtained by the angle FFT.

[0043] By power-transforming the distance data sequence, a distance power spectrum with n / 2 bins is obtained. The distance power spectrum includes two peak bins corresponding to the distances to the object 210a and the object 210b.

[0044] A velocity power spectrum with m bins is obtained by performing a velocity FFT on the data sequence corresponding to the peak bin position of the distance power spectrum and power transforming the newly obtained velocity data sequence. The velocity power spectrum includes a peak bin corresponding to the velocity of object 210a or object 210b. Which living organism's velocity it corresponds to depends on the peak bin position of the selected distance power spectrum.

[0045] An angle power spectrum with k bins is obtained by performing an angle FFT on the data sequence corresponding to the peak bin position of the distance power spectrum and power converting the newly obtained angle data sequence. The angle power spectrum includes a peak bin corresponding to the angle of object 210a or object 210b. Which biological angle the peak bin corresponds to depends on the peak bin position of the selected distance power spectrum.

[0046] The information processing device 500 calculates, in time series, phase information obtained by phase-transforming the distance data sequence of the object 210 obtained by the distance FFT, and acquires biosignal data of the object 210. Note that the bin position of the phase information used as the biosignal information corresponds to the peak bin position of the distance power spectrum. The biosignal data is an example of an output signal of the information processing device 500.

[0047] 3A is a diagram for explaining the principle of detecting the distance R to the object 210. The distance R to the object 210 is calculated by performing a distance FFT of at least one chirp. The solid line in the graph represents the transmitted wave 220, and the dashed line represents the received wave 230. The vertical axis represents frequency, and the horizontal axis represents time.

[0048] The IF frequency is the frequency of the beat signal 150 obtained by mixing the transmission wave 220 and the reception wave 230 of the FMCW radar. The IF frequency increases as the distance R between the information processing device 500 and the target 210 increases. The information processing device 500 can obtain an IF frequency proportional to the distance R to the target 210 for a certain period of time. That is, the information processing device 500 can obtain the distance R to the target 210 by analyzing the IF frequency.

[0049] The information processing device 500 performs distance FFT processing for each chirp. For example, if the number of FFT points is n, n / 2 points of real and imaginary data sequences are obtained, resulting in n / 2 bins. The information processing device 500 calculates a peak bin by performing power conversion based on the results of the distance FFT. The information processing device 500 can calculate the distance R to the object 210 based on the frequency at which the peak appears.

[0050] FIG. 3B is a diagram illustrating the principle of detecting the velocity V of the object 210. For each data sequence obtained by the distance FFT for multiple chirps in a burst, a velocity FFT is performed on a new data sequence corresponding to the peak bin position of the distance power spectrum. One burst contains m chirps. The velocity V of the object 210 is calculated by performing a power transform based on the results of the velocity FFT.

[0051] The information processing device 500 performs velocity FFT processing for one burst. For example, if the number of chirps in one burst is m, m points of real and imaginary data sequences are obtained, resulting in m bins. The information processing device 500 calculates the peak bin position by performing power conversion based on the velocity FFT results. The information processing device 500 can calculate the velocity V from the frequency of the peak bin of the velocity FFT.

[0052] 3C is a diagram illustrating the principle of detecting the angle θ of the object 210. The angle θ is determined by performing an angle FFT on each data sequence obtained by the distance FFT for the chirp of each channel, and then performing an angle FFT on a new data sequence corresponding to the peak bin position of the distance power spectrum. One chirp contains k chirps corresponding to k channels.

[0053] The information processing device 500 performs angle FFT processing for k channels. For example, in the case of k channels, k points of data sequences of real and imaginary parts are obtained, and the number of bins is k. The information processing device 500 receives the received waves 230 using the receiving units 140 of k channels arranged in the X-axis direction. A phase difference corresponding to the angle θ of the object 210 occurs in the received waves 230 received by each receiving unit 140, and therefore the information processing device 500 can calculate the angle θ of the object 210 by analyzing the received signals of the k channels.

[0054] 4A is a diagram illustrating a method for sensing a moving object 210. The movement of the object 210 is larger than a biological signal such as a heartbeat. When the object 210 moves, the position of the peak bin of the distance power spectrum may move. When the object 210 is not in the same bin and the bin is changed, a phase discontinuity (i.e., a phase shift) may occur.

[0055] The information processing device 500 can obtain accurate distance information of the object 210 by tracking the peak bin of the distance FFT, which is the basic principle of biological sensing using FMCW radar. On the other hand, even if the peak bin changes, tracking only the same bin may result in a deterioration in the accuracy of the phase information obtained compared to the peak bin. The information processing device 500 of this example can suppress such deterioration in the accuracy of the phase information by compensating for the phase shift.

[0056] FIG. 4B shows an example of the effect of body movement of the object 210. To extract such body movement, phase is acquired at multiple points of the chirp. However, if the peak bin from which the phase value is acquired changes, the phase signal becomes discontinuous, and a phase shift of approximately π [rad] may occur. In this example, the bin number of the peak bin shifts from 29 to 30 during the period from 1 second to 3 seconds, resulting in a phase shift.

[0057] 5A shows an example of the configuration of signal processing unit 400. Signal processing unit 400 includes a data processing unit 410, an acquisition unit 420, an extraction unit 430, and a correction unit 440.

[0058] The data processing unit 410 processes the received signal based on the received wave 230 to obtain a power spectrum signal with a predetermined number of bins. In this example, the data processing unit 410 performs at least one of a distance FFT, a velocity FFT, and an angle FFT to generate the power spectrum signal. Note that the data processing unit 410 may process the received signal using other algorithms such as the CAPON method or compressed sensing.

[0059] The acquiring unit 420 acquires a peak bin corresponding to the object 210 based on the power spectrum signal output by the data processing unit 410. When the bin number of the peak bin to which the object 210 belongs is changed, the acquiring unit 420 may change the peak bin to be acquired.

[0060] The extracting unit 430 extracts a predetermined output signal from the power spectrum signal. The extracting unit 430 of this example extracts IQ data including an in-phase component and a quadrature component orthogonal to the in-phase component, based on the peak bin acquired by the acquiring unit 420.

[0061] The correction unit 440 executes a phase correction algorithm based on the IQ data extracted by the extraction unit 430 and the detection result of the acquisition unit 420. The correction unit 440 corrects the phase of the output signal according to the bin numbers of the multiple peak bins. This allows the correction unit 440 to suppress the influence of a phase shift that occurs when the bin number to which the object 210 belongs changes over time. The specific operation of the correction unit 440 will be described later.

[0062] 5B shows an example of the operation of the signal processing unit 400. The signal processing unit 400 receives a digital received signal that has been AD converted by the input unit 300.

[0063] The data processing unit 410 executes distance FFT processing and window function processing. The data processing unit 410 executes distance FFT on the received signal to acquire a distance power spectrum. The data processing unit 410 outputs the generated distance power spectrum to the acquisition unit 420.

[0064] The acquisition unit 420 acquires a peak bin indicating the distance R from the object 210 based on the power spectrum signal. The acquisition unit 420 may acquire the peak position using a simple peak detection algorithm, or may acquire the peak position using another algorithm such as a CFAR algorithm. The acquisition unit 420 outputs the acquired information regarding the distance R from the object 210 to the extraction unit 430 and the correction unit 440.

[0065] The extraction unit 430 acquires IQ data corresponding to the peak bin acquired by the acquisition unit 420 from the result of the distance FFT performed by the data processing unit 410. The extraction unit 430 may extract the IQ data directly from the power spectrum signal, or may use other algorithms such as compressed sensing. The extraction unit 430 outputs the IQ data extraction result to the correction unit 440.

[0066] The correction unit 440 corrects the phase of the output signal from the data processing unit 410. In this example, the correction unit 440 corrects the phase of the output signal based on the distance acquisition result after the distance FFT. For example, the correction unit 440 corrects the phase of the peak bin of the distance power spectrum according to the bin number of the peak bin acquired by the distance FFT.

[0067] 5C is a diagram for explaining the detailed configuration of data processing unit 410 and acquisition unit 420. Data processing unit 410 has window function execution unit 411 and distance FFT execution unit 412.

[0068] The window function execution unit 411 applies a predetermined window function to the received digital signal. The window function execution unit 411 may use any window function such as a rectangular window, a Hann window, a Hamming window, or a Blackman window. The window function execution unit 411 may extract the received signal using a window function of a higher order than a rectangular window. The Hann window, the Hamming window, and the Blackman window are examples of window functions of a higher order than a rectangular window.

[0069] In this example, after the window function execution unit 411 extracts the digital received signal, the distance FFT execution unit 412 performs a distance FFT to convert it into a distance power spectrum in the frequency domain. However, the order of the FFT processing and the window function processing is not limited to this. The window function execution unit 411 may extract the signal after the distance FFT execution unit 412 performs a distance FFT to convert it into a distance power spectrum in the frequency domain.

[0070] The acquisition unit 420 has a predetermined peak detection algorithm. In this example, the acquisition unit 420 has a simple detection algorithm for local peak search. However, the acquisition unit 420 may use other more complex detection algorithms such as CFAR.

[0071] 5D shows an example of a phase correction algorithm of the correction unit 440. The correction unit 440 includes a phase selection unit 441 and a rotation unit 442.

[0072] The phase selection unit 441 selects a phase of 0 or π based on the bin number of the peak bin of the distance power spectrum. In one example, the phase selection unit 441 selects 0 when the bin number is odd, and selects π when the bin number is even. Alternatively, the phase selection unit 441 may select π when the bin number is odd, and select 0 when the bin number is even.

[0073] The rotation unit 442 rotates the phase of the IQ data by the phase selected by the phase selection unit 441. This allows the correction unit 440 to correct the phase of the peak bin in accordance with a change in the bin number of the peak bin.

[0074] 5E shows a modified example of the phase correction algorithm of the correction unit 440. The correction unit 440 has a phase selection unit 441, a phase conversion unit 443, and an addition / subtraction unit 444. The correction unit 440 of this example corrects the phase of the peak bin with a phase magnitude depending on whether the bin number is odd or even.

[0075] The phase conversion unit 443 converts the IQ data into phase data. The phase conversion unit 443 in this example converts the IQ data into tan -1 The phase data is converted by calculating (Q / I).

[0076] The adding / subtracting unit 444 adds or subtracts 0 or π to the phase data converted by the phase converting unit 443, based on the bin number acquired by the acquiring unit 420. In one example, the phase selecting unit 441 selects 0 when the bin number is odd, and selects π when the bin number is even. Alternatively, the phase selecting unit 441 may select π when the bin number is odd, and select 0 when the bin number is even.

[0077] The data processing unit 410 may apply a window function of higher order than a rectangular window to the received signal. The correction unit 440 may correct the phase of the output signal so that the difference between the phase to be added to or subtracted from odd-numbered bin numbers and the phase to be added to or subtracted from even-numbered bin numbers becomes (2×i+1)π [rad]. i may be any integer.

[0078] Here, applying a high-order window function to the received signal may cause the phase to be inverted by π between odd-numbered bins and even-numbered bins. Therefore, by having the correction unit 440 determine a correction value such that the difference between the correction coefficients for odd-numbered bins and even-numbered bins is (2×i+1)π [rad], it becomes unnecessary to calculate the correction value each time, thereby simplifying the correction calculation process. This makes it easy for the information processing device 500 to identify the position of the bin, and even if the peak bin is shifted to an adjacent bin, it can easily correct the phase and maintain phase continuity.

[0079] FIG. 6A shows an example of the relationship between signal intensity and phase with respect to the bin number. In this example, the data processing unit 410 uses a rectangular window as a window function. A phase shift of π occurs at the bin number corresponding to the intensity peak. The behavior of the phase change around the intensity peak also varies depending on the window function used. When a rectangular window is used as the window function, one phase shift occurs around the intensity peak.

[0080] 6B shows an example of the relationship between signal intensity and phase with respect to the bin number. In this example, the data processing unit 410 uses a Hann window as the window function. In this example, more phase shifts occur than when a rectangular window is used. When a Hann window is used as the window function, multiple phase shifts occur around the intensity peak.

[0081] 6C shows an example of the relationship between signal intensity and phase with respect to the bin number. In this example, the data processing unit 410 uses a Hamming window as the window function. When a Hamming window is used as the window function, multiple phase shifts occur around the intensity peak. In this example, more phase shifts occur than when a Hann window is used.

[0082] 6D shows an example of the relationship between signal intensity and phase with respect to the bin number. In this example, the data processing unit 410 uses a Blackman window as the window function. When a Blackman window is used as the window function, multiple phase shifts occur around the intensity peak. In this example, more phase shifts occur than when a Hamming window is used.

[0083] Thus, window functions other than the rectangular window cause more phase shift than the rectangular window. Also, the higher the order of the window function, the more phase shift occurs. The direction of the phase shift change is determined by the bin numbers before and after the change. Note that the data processing unit 410 may use any window function other than the rectangular window, Hann window, Hamming window, and Blackman window.

[0084] Note that by using a window function of a higher order than a rectangular window, the information processing device 500 can more easily identify the position of the bin in which the object 210 exists, even if the object 210 moves. When multiple objects 210 exist, an optimal window function may be selected as appropriate depending on the distance between the objects.

[0085] 7A shows a modified example of the signal processing unit 400. In this example, differences from the embodiment in FIG. 5B will be particularly described. Other points may be the same as in FIG. 5B. In this example, the data processing unit 410 performs a velocity FFT and an angle FFT in addition to a distance FFT.

[0086] The data processing unit 410 acquires a velocity power spectrum by performing a velocity FFT on the received signal. The data processing unit 410 also acquires an angle power spectrum by performing an angle FFT on the received signal. After performing the distance FFT, the data processing unit 410 in this example performs a velocity FFT and an angle FFT based on the data sequence corresponding to the peak bin position identified by the distance FFT.

[0087] The acquisition unit 420 acquires peak bins related to the velocity V and angle θ in addition to the distance R of the object 210. The acquisition unit 420 acquires peak bins indicating the velocity V of the object 210 based on the velocity power spectrum. The acquisition unit 420 of this example acquires peak bins of the velocity FFT based on a data string corresponding to the peak bin position identified by the distance FFT. This makes it possible to acquire the velocity V of any object 210 selected according to the distance R.

[0088] Furthermore, the acquiring unit 420 acquires a peak bin indicating the angle θ with respect to the object 210 based on the angle power spectrum. The acquiring unit 420 in this example acquires the peak bin of the angle FFT based on a data string corresponding to the peak bin position identified by the distance FFT. This makes it possible to acquire the angle θ of any object 210 selected according to the distance R.

[0089] The extraction unit 430 extracts each IQ data based on the peak bin indicating the distance R, velocity V, and angle θ of the object 210. In this example, the extraction unit 430 extracts the IQ data from the velocity V and angle θ of the bin corresponding to the peak bin of the distance R of the object 210. The extraction unit 430 may extract the IQ data directly or may extract the IQ data using another algorithm such as compressed sensing.

[0090] The correction unit 440 executes a phase correction algorithm based on the IQ data extracted from at least one of the distance R, the velocity V, and the angle θ extracted by the extraction unit 430. For example, the correction unit 440 corrects the phase of each of the output signals of the distance R, the velocity V, and the angle θ based on the IQ data extracted from the distance R, the velocity V, and the angle θ of the object 210.

[0091] The information processing device 500 of this example can output output signals with the phase corrected for each of the distance R, the velocity V, and the angle θ. The information processing device 500 may correct only the phase of the output signal for either the distance R, the velocity V, or the angle θ.

[0092] 7B is a diagram illustrating the detailed configuration of data processing unit 410 and acquisition unit 420. In this example, processing related to velocity FFT and angle FFT will be described. Data processing unit 410 in this example has a window function execution unit 413, a velocity FFT execution unit 414, a window function execution unit 415, and an angle FFT execution unit 416.

[0093] The window function execution unit 413 executes a predetermined window function to extract the input signal. The velocity FFT execution unit 414 performs velocity FFT processing on the signal extracted by the window function execution unit 413, converting it into a velocity power spectrum in the frequency domain.

[0094] In this example, after the window function execution unit 413 extracts the digital received signal, the rate FFT execution unit 414 performs a rate FFT to convert it into a rate power spectrum in the frequency domain. However, the order of the FFT processing and the window function processing is not limited to this. The rate FFT execution unit 414 may convert it into a rate power spectrum, and then the window function execution unit 413 may extract the signal. Other algorithms such as the CAPON method or compressed sensing may be used for the spectrum conversion.

[0095] The window function execution unit 415 executes a predetermined window function to extract the signal input from the velocity FFT execution unit 414. The angle FFT execution unit 416 performs angle FFT processing on the signal extracted by the window function execution unit 415 to convert it into an angular power spectrum in the frequency domain. The window function execution unit 415 may use the same window function as the window function execution unit 413, or may use a different window function.

[0096] In this example, after the window function execution unit 415 extracts the digital received signal, the angle FFT execution unit 416 performs angle FFT to convert it into an angular power spectrum in the frequency domain. However, the order of the FFT processing and window function processing is not limited to this. The window function execution unit 415 may extract the signal after the angle FFT execution unit 416 converts it into an angular power spectrum. Other algorithms such as the CAPON method or compressed sensing may be used for the spectrum conversion.

[0097] The acquisition unit 420 detects peak positions indicating the velocity V or angle θ of the object 210 from the frequency domain power spectrum signals output by the velocity FFT execution unit 414 and the angle FFT execution unit 416 using a predetermined detection algorithm. The acquisition unit 420 may acquire the peak positions using a simple peak detection algorithm, or may acquire the peak positions using other algorithms such as the CFAR algorithm. The acquisition unit 420 outputs peak bins for the acquired velocity V and angle θ of the object 210 to the extraction unit 430 and the correction unit 440.

[0098] 8A shows a modified example of the signal processing unit 400. In this example, differences from the embodiment in FIG. 7A will be particularly described. Other points may be the same as in FIG. 7A. The signal processing unit 400 of this example has a data control unit 450. The data control unit 450 has a data conversion unit 452 and a tracking unit 454.

[0099] The data conversion unit 452 converts multiple pieces of data corresponding to the object 210 acquired by the acquisition unit 420 into clustered data. For example, when multiple detection points that may belong to the same object 210 occur, the data conversion unit 452 replaces the multiple detection points corresponding to the object 210 with one detection point. This allows the information processing device 500 to cluster groups corresponding to the same object 210 into one detection point, thereby simplifying processing.

[0100] When data corresponding to the object 210 is not obtained within a predetermined period of time, the tracking unit 454 tracks the object 210 based on past data of the object 210. Even when the tracking unit 454 is unable to detect the object 210 through processing within a predetermined period of time and data is missing in the detection of the vibration of the object 210, the tracking unit 454 can complement the data of the object 210 by using a tracking algorithm. This allows the information processing device 500 to predict the position of the object 210 even when no measurement data is available within a predetermined period of time.

[0101] In this example, data control unit 450 outputs the clustered data to extraction unit 430 and correction unit 440, which simplifies the processing in extraction unit 430 and correction unit 440. Furthermore, data control unit 450 outputs tracked data to extraction unit 430 and correction unit 440, which makes it possible to avoid data loss in extraction unit 430 and correction unit 440 as well.

[0102] 8B shows an example of the operation of the signal processing unit 400. The data control unit 450 may perform a clustering process or a tracking process on the detected points corresponding to at least one of the distance R, the velocity V, and the angle θ of the object 210. The data control unit 450 outputs the processed data to the extraction unit 430 and the correction unit 440, respectively.

[0103] 8C shows an example of data conversion by data control unit 450. The data conversion method in this example is an example and is not limited to this.

[0104] In step S100, the data control unit 450 clusters the acquired data acquired by the acquisition unit 420. For example, the acquired data is clustered into clusters according to the distribution of the acquired data. In this example, the data control unit 450 clusters the acquired data into three clusters. The data control unit 450 sets representative values ​​d1 to d3 that represent each cluster. The representative values ​​may be values ​​near the center of the cluster distribution.

[0105] In step S102, the calculated representative value is associated with the past tracked representative values ​​t1 to t3 to form a pair of the new representative value and the past representative value. In this example, the representative value d2 is associated with the past representative value t1, and the representative value d3 is associated with the past representative value t2.

[0106] In step S104, the data control unit 450 manages the target to link the correct ID to the associated data.

[0107] Since there is no corresponding representative value for representative value d1, a new ID is assigned to the new object 210. Since there are past tracked representative values ​​t1 and t2 for representative values ​​d2 and d3, respectively, they are associated with the ID corresponding to the object 210. Since there is no representative value corresponding to the current representative value for past representative value t3, it is removed.

[0108] In step S106, the representative values ​​t1, t2, and t4 are filtered using a Kalman filter. In this example, new tracked representative values ​​t1, t2, and t4 are present. The representative values ​​t1 and t2 continue to exist as tracked representative values. The representative value t4 is tracked as a new representative value based on the representative value d1. The tracked representative values ​​t1, t2, and t4 may be used for target association in step S102.

[0109] Depending on the detection algorithm or tracking algorithm, adjacent bins may be selected. Even in such a case where adjacent bins are selected and a phase shift occurs, the information processing device 500 can compensate for the phase shift of the output signal.

[0110] FIG. 9 shows an example of the result of phase compensation by the information processing device 500. By compensating the phase, the information processing device 500 can remove phase discontinuity compared to the case without phase compensation. The correction unit 440 of this example corrects the phase of the peak bin with bin number 30 without correcting the phase of the peak bin with bin number 29. Note that the bin numbers for which phase correction is performed are not limited to this. Bin number 29 is an example of a first bin number for which phase correction is not performed. Bin number 30 is an example of a second bin number for which phase correction is performed.

[0111] The information processing device 500 can compensate for the phase shift caused by the movement of the object 210 and sense the object 210 more accurately over time. The information processing device 500 can detect small body movements of the object 210 without contact and can be used in various fields. The information processing device 500 can be applied to the medical field to detect biological signals such as heartbeat or respiration. In addition, the information processing device 500 may be used to detect defects by sensing vibrations in structures such as buildings or bridges, or may be used to sense vibrations in motors, etc.

[0112] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0113] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0114] 38...Data cube, 100...Transmitting and receiving unit, 120...Transmitting unit, 140...Receiver, 150...Beat signal, 160...Transmitting and receiving control unit, 210...Object, 220...Transmitted wave, 230...Received wave, 300...Input unit, 400...Signal processing unit, 410...Data processing unit, 411...Window function execution unit, 412...Distance FFT execution unit, 413...Window function execution unit, 4 14... Velocity FFT execution unit, 415... Window function execution unit, 416... Angle FFT execution unit, 420... Acquisition unit, 430... Extraction unit, 440... Correction unit, 441... Phase selection unit, 442... Rotation unit, 443... Phase conversion unit, 444... Addition / subtraction unit, 450... Data control unit, 452... Data conversion unit, 454... Tracking unit, 500... Information processing device, 600... System

Claims

1. An information processing device that senses an object using an FMCW radar, a data processing unit that processes a received signal based on a received wave of the FMCW radar to generate a power spectrum signal with a predetermined number of bins; an acquisition unit that acquires a plurality of peak bins corresponding to the object based on the power spectrum signal; an extractor that extracts an output signal corresponding to the power spectrum signals of the plurality of peak bins; a correction unit that corrects the phase of the output signal in accordance with the bin numbers of the plurality of peak bins; Equipped with the data processing unit applies a window function of a higher order than a rectangular window to the received signal; the correction unit corrects the phase of the output signal so that a difference between a phase to be added to or subtracted from the odd-numbered bin numbers and a phase to be added to or subtracted from the even-numbered bin numbers becomes (2×i+1)π [rad]; i is an arbitrary integer Information processing device.

2. the plurality of peak bins include a peak bin with a predetermined first bin number and a peak bin with a second bin number different from the first bin number, The correction unit corrects the phase of the peak bin having the second bin number without correcting the phase of the peak bin having the first bin number. The information processing device according to claim 1 .

3. the data processing unit performs a distance FFT on the received signal to obtain a distance power spectrum relating to the distance to the object; The acquisition unit acquires a peak bin indicating a distance to the object based on the distance power spectrum.

3. The information processing device according to claim 1.

4. The correction unit corrects the phase of a peak bin of the distance power spectrum. The information processing device according to claim 3 .

5. the data processing unit performs a velocity FFT on the received signal to obtain a velocity power spectrum relating to the velocity of the object; The acquisition unit acquires a peak bin indicating a velocity of the object based on the velocity power spectrum.

3. The information processing device according to claim 1.

6. The acquisition unit acquires a peak bin of the velocity FFT based on a data string corresponding to a peak bin position identified by the distance FFT. The information processing device according to claim 5 .

7. the data processing unit performs an angular FFT on the received signal to obtain an angular power spectrum relating to an angle with the object; The acquisition unit acquires a peak bin indicating an angle with respect to the object based on the angle power spectrum.

3. The information processing device according to claim 1.

8. The acquisition unit acquires a peak bin of the angle FFT based on a data string corresponding to a peak bin position identified by the distance FFT. The information processing device according to claim 7 .

9. a data conversion unit that converts a plurality of data corresponding to the object into clustered data; 3. The information processing device according to claim 1.

10. A tracking unit is provided that tracks the object based on past data of the object when data corresponding to the object is not obtained within a predetermined period of time.

3. The information processing device according to claim 1.

11. The extracting unit extracts IQ data including an in-phase component and a quadrature component orthogonal to the in-phase component from the peak bin acquired by the acquiring unit.

3. The information processing device according to claim 1.

12. The data processing unit processes the received signal using a CAPON method or a compressed sensing algorithm.

3. The information processing device according to claim 1.

13. A sensing method for sensing an object using an FMCW radar, comprising: processing a received signal based on a received wave of the FMCW radar to generate a power spectrum signal having a predetermined number of bins; obtaining a plurality of peak bins corresponding to the object based on the power spectrum signal; extracting an output signal corresponding to the power spectrum signals of the plurality of peak bins; correcting a phase of the output signal according to bin numbers of the plurality of peak bins; Equipped with generating the power spectrum signal includes applying a window function of a higher order than a rectangular window to the received signal; the correcting step includes a step of correcting the phase of the output signal so that a difference between a phase to be added to or subtracted from the odd-numbered bin numbers and a phase to be added to or subtracted from the even-numbered bin numbers becomes (2×i+1)π [rad], i is a sensing method that can be any integer.

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