A human body sensing detection method based on FMCW millimeter wave radar

Abstract

The application discloses a human body sensing detection method based on FMCW millimeter wave radar, which comprises the following steps: collecting and processing data of an environment by using FMCW millimeter wave radar, obtaining a plurality of frames of original intermediate frequency signal data, and performing frame processing to obtain adaptive speed amplitude thresholds and target speed energy amplitudes of corresponding frames, then using different strategies for detection according to a human body sensing detection result of a previous frame: if the human body sensing detection result of the previous frame is a large-amplitude action human body target or a stationary human body target, whether the target speed energy amplitude of a current frame is greater than the adaptive speed amplitude threshold is judged, if yes, it is judged that the current frame is a large-amplitude action human body target; otherwise, human body vital sign detection is performed to obtain target respiration signals and heartbeat signals, and whether the target is a human body target is judged; if the human body sensing detection result of the previous frame is no large-amplitude action human body target or a non-human body target, whether the target speed energy amplitude of the current frame is greater than the adaptive speed amplitude threshold is judged, if yes, it is judged that the current frame is a large-amplitude action human body target; otherwise, it is judged that the current frame is a non-large-amplitude action human body target. The application can detect stationary human body targets and human body targets with large-amplitude actions, and realize more accurate human body sensing detection in different scenes.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing technology, and more specifically, to a human body perception detection method based on FMCW millimeter wave radar. Background Art

[0002] Radar target detection is a technology that uses radar systems to detect and identify targets. Its basic operating principle is that the radar system's RF front-end emits an electromagnetic wave signal through the transmitting antenna. The electromagnetic wave signal is reflected by the target object and captured by the receiving antenna. Digital signal processing is then performed to obtain target information, completing target detection. In the past, sensors only needed to determine the presence of the target. However, with the rapid development of technology, in order to meet real-world needs, it is also necessary to obtain various information such as the target's distance, speed, and angle for accurate target detection and identification.

[0003] FMCW (Frequency Modulated Continuous Wave) millimeter-wave radar offers significant advantages in target detection. It leverages the unique frequency variation characteristics of electromagnetic waves to acquire target information by emitting chirp signals whose frequency varies linearly with time. This technology offers long-range detection capabilities and high resolution, enabling high-precision target detection and tracking in complex environments. Furthermore, FMCW millimeter-wave radar is highly sensitive to subtle movements, such as the minute amplitude of chest movements caused by breathing and heartbeats, making it suitable for detecting vital signs.

[0004] However, existing technical methods have certain limitations. Frequency-domain analysis-based FFT algorithms extract the target's frequency characteristics to obtain distance and speed information for human target detection. While this algorithm can identify human targets making large movements, it is insufficient to effectively identify stationary human targets and may face issues with false detection and missed detection. While human vital sign detection methods can effectively identify stationary human targets, collecting phase change information from the target's chest cavity requires a certain amount of time, resulting in a longer response time for human target detection. Furthermore, when a human target makes large movements, radar has difficulty extracting the tiny amplitude of the human chest cavity, resulting in poor detection performance. Furthermore, generally speaking, people have high expectations for radar response speed when switching from unmanned to present, and low expectations for response speed when switching from present to unmanned. Therefore, different strategies should be used for human detection in different scenarios. Summary of the Invention

[0005] In order to overcome the defect of the above-mentioned prior art that it cannot simultaneously detect stationary human targets and human targets with large movements, the present invention provides a human perception detection method based on FMCW millimeter wave radar.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] The present invention proposes a human body perception detection method based on FMCW millimeter wave radar, comprising the following steps:

[0008] S1: Use FMCW millimeter-wave radar to collect data on the environment, obtain radar echo signals, perform discrete sampling on the radar echo signals, and obtain several frames of original intermediate frequency signal data;

[0009] S2: Frame processing is performed on each frame of the original intermediate frequency signal data to obtain the adaptive speed amplitude threshold and target speed energy amplitude of the corresponding frame;

[0010] S3: Outputting several human body perception detection results of the radar echo signal according to the adaptive velocity amplitude threshold and target velocity energy amplitude of each frame of original intermediate frequency signal data.

[0011] Preferably, step S2 includes:

[0012] S2.1: performing frame reorganization on each frame of the original intermediate frequency signal data to obtain an initial two-dimensional matrix of the corresponding frame, performing mean cancellation on the initial two-dimensional moments to obtain a denoised two-dimensional matrix;

[0013] S2.2: Process the denoised two-dimensional matrix to obtain a distance information matrix, and then perform non-correlated accumulation calculation on the distance information matrix to obtain a target range gate;

[0014] S2.3: Perform static clutter filtering on the distance information matrix to obtain a de-cluttered distance information matrix, and obtain an adaptive velocity amplitude threshold and a target velocity energy amplitude based on the de-cluttered distance information matrix and the target range gate.

[0015] Preferably, in S2.1, performing mean cancellation on the initial two-dimensional moments to obtain a denoised two-dimensional matrix includes:

[0016] The initial two-dimensional matrix of the original intermediate frequency signal data of the i-th frame is recorded as R i , the size is M*N, where M is the number of fast time sampling points and N is the number of slow time sampling points. i Sum and average each row of , and obtain the mean matrix C of size M*1 i , specifically:

[0017]

[0018] Among them, m represents the row index, C i [m] means C i The data of the mth index in;

[0019] The matrix Ri Each column of data is subtracted from the mean matrix C i [m], obtain the denoised two-dimensional matrix:

[0020] R i1 [m,n]=R i [m,n]-C i [m], m=1,2,…,M, n=1,2,…,N

[0021] Among them, R i1 The two-dimensional matrix representing the denoised data of the original IF signal of the i-th frame, where n represents the column index.

[0022] Preferably, in S2.2, processing the denoised two-dimensional matrix to obtain a distance information matrix includes:

[0023] The denoised two-dimensional matrix is ​​subjected to distance dimension fast Fourier transform along the fast time axis to obtain the distance transformation matrix, and then the distance transformation matrix is ​​processed by adding Hanning window to obtain the distance information matrix.

[0024] Preferably, in S2.3, obtaining the adaptive velocity amplitude threshold and the target velocity energy amplitude according to the clutter removal range information matrix and the target range gate includes:

[0025] The clutter-free range information matrix is ​​subjected to velocity-dimensional fast Fourier transform and filtering along the slow time axis to obtain the range-Doppler matrix.

[0026] Perform unit-averaged constant false alarm target detection on the range-Doppler matrix along the slow time axis to obtain the adaptive velocity amplitude threshold, specifically:

[0027] Determine the size of the training area and protection area for each row of data in the range Doppler matrix, and set the length of the training unit in the training area to N train =4, the protection unit length is N guard =8;

[0028] Calculate the average signal strength of the training unit as the clutter noise power statistic Z of the unit to be detected;

[0029] Calculate the adaptive speed amplitude threshold T. The specific calculation formula is:

[0030]

[0031] Among them, P fa =1e -6 represents the false alarm probability, N train represents the length of the training unit, and Z represents the average signal strength of the training unit;

[0032] The target range gate is used to extract data from the range Doppler matrix to obtain the target velocity energy amplitude.

[0033] Preferably, the S3 includes:

[0034] S3.1: Determine whether the target velocity energy amplitude of the first frame of the original intermediate frequency signal data is greater than an adaptive velocity amplitude threshold. If so, the human perception detection result for the first frame is that the target is moving with large amplitude. If so, the human perception detection result for the first frame is that the target is not moving with large amplitude.

[0035] S3.2: Let i = 2;

[0036] S3.3: Based on the human perception detection result of the previous frame of raw IF signal data, use different strategies to detect the raw IF signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large-scale movements, execute step S3.4; if the human perception detection result of the i-1th frame is a human target without large-scale movements, execute step S3.6;

[0037] S3.4: Determine whether the target velocity energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive velocity amplitude threshold. If it is greater than the threshold, the human perception detection result of the i-th frame is a human target with large-amplitude movements. If it is less than the threshold, further determine whether i+L-1>I, where L is the human vital sign detection interval and I is the number of frames of the original intermediate frequency signal data. If so, the human perception detection result of the i-th frame is no human target with large-amplitude movements, and execute step S3.7. Otherwise, extract human vital sign information from the target phase information of the original intermediate frequency signal data of L frames, from the i-th frame to the i+L-1-th frame, and perform vital sign detection and judgment based on the human vital sign information. If it is determined that there are human vital signs, the human perception detection result of the i+L-1-th frame is a stationary human target. If it is determined that there are no human vital signs, the human perception detection result of the i+L-1-th frame is no human target.

[0038] S3.5: Execute i=i+L and determine if i>I. If so, proceed to step S3.9; otherwise, proceed to step S3.8.

[0039] S3.6: Determine whether the target velocity energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive velocity amplitude threshold. If so, the human perception detection result of the i-th frame is a human target with large-amplitude motion. If so, the human perception detection result of the i-th frame is a human target with no large-amplitude motion.

[0040] S3.7: execute i = i + 1, judge i > I, if yes, execute step S3.9, otherwise, further judge i > L, if yes, execute step S3.8, otherwise, return to step S3.3;

[0041] S3.8: according to the human perception detection result of the last frame of original intermediate frequency signal data, use different strategies to detect the current frame of original intermediate frequency signal data, if the human perception detection result of the i-1 frame is a large amplitude motion human target or a static human target, return to step S3.4; if the human perception detection result of the i-1 frame is no large amplitude motion human target or non-human target, return to step S3.6;

[0042] S3.9: obtain a plurality of human perception detection results of radar echo signals.

[0043] Preferably, in S3.4, the target phase information of the L frames of original intermediate frequency signal data from the i frame to the i+L-1 frame is extracted to obtain human vital sign information, and the vital sign detection judgment is performed according to the human vital sign information, which comprises:

[0044] obtaining a discrete sequence Phi containing target chest cavity phase information of the L frames of original intermediate frequency signal data from the i frame to the i+L-1 frame,

[0045] Phi = [Data i , Data i+1， …, Data i+L-1 ]

[0046] Wherein, Data i represents a discrete point containing target chest cavity phase information in the i frame of original intermediate frequency signal data;

[0047] Phase inverse tangent is performed on Phi to extract the corresponding target phase signal, and phase unwrapping is performed on the target phase signal to obtain a phase unwrapping signal:

[0048]

[0049] Wherein, φ unwrap (i) represents the target phase unwrapping signal of the i frame of original intermediate frequency signal data, and φ(i) represents the target phase signal of the i frame of original intermediate frequency signal data;

[0050] Phase difference processing is performed on the target phase unwrapping signal to obtain a target vital sign signal:

[0051] φ diff (i) = φ unwrap (i+1) - φ unwrap (i), i = 1, 2, …, L-2

[0052] Among them, φ diff (i) represents the target vital sign signal of the original intermediate frequency signal data of the i-th frame;

[0053] A smoothing algorithm is used to smooth the target vital sign signal of the original intermediate frequency signal data from the i-th frame to the i+L-1-th frame (a total of L frames) to obtain the target vital sign smoothed signal. A first elliptical bandpass filter and a second elliptical bandpass filter are designed, and the target vital sign smoothed signal is input into the first elliptical bandpass filter and the second elliptical bandpass filter respectively to obtain the corresponding target breathing signal and heartbeat signal. The target breathing signal and heartbeat signal are detected based on the detection to determine whether the target is a human target.

[0054] Preferably, the design parameters of the first elliptical bandpass filter and the second elliptical bandpass filter include: passband frequency of 0.2Hz-0.6Hz, stopband boundary frequency of 0.1Hz-0.7Hz, passband ripple of 0.2dB, and stopband minimum attenuation of 30dB.

[0055] Preferably, the design parameters of the second elliptical bandpass filter include: passband frequency of 0.8Hz-2.0Hz, stopband boundary frequency of 0.7Hz-2.1Hz, passband ripple of 0.2dB, and stopband minimum attenuation of 30dB.

[0056] Preferably, the step of determining whether the target is a human target based on the target breathing signal and heartbeat signal detection includes:

[0057] Perform fast Fourier transform on the target respiratory signal and heartbeat signal to obtain the target respiratory frequency domain signal and heartbeat frequency domain signal. Calculate the spectrum amplitude of the target respiratory frequency domain signal and heartbeat frequency domain signal respectively, and set the judgment conditions 1 to 4:

[0058] Condition 1: The peak value of the spectrum amplitude of the target respiratory frequency domain signal is greater than the spectrum amplitude threshold of the target respiratory frequency domain signal;

[0059] Condition 2: The peak amplitude position of the target respiratory frequency domain signal is in the range of 0.2Hz-0.6Hz;

[0060] Condition 3: The peak value of the spectrum amplitude of the target heartbeat frequency domain signal is greater than the spectrum amplitude threshold of the target heartbeat frequency domain signal;

[0061] Condition 4: The peak amplitude position of the target heartbeat frequency domain signal is in the range of 0.8 Hz to 2.0 Hz.

[0062] If conditions 1, 2, 3, and 4 are all met, the target is judged to be a stationary human target; otherwise, the target is judged to be a non-human target.

[0063] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0064] The present invention proposes a human perception detection method based on FMCW millimeter-wave radar. The method can detect stationary human targets and human targets with large movements. The method uses target velocity information to distinguish whether there are human targets with large movements. The method then combines target vital sign detection to detect stationary human targets. Different detection strategies are applied to the current frame based on the detection results of the previous frame, achieving rapid judgment from unmanned to occupied and accurate judgment from occupied to unmanned. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of the human body perception detection method described in Example 1;

[0066] Figure 2 This is a flow chart of different strategy detection described in Example 3. DETAILED DESCRIPTION

[0067] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0068] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0069] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0070] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0071] Example 1

[0072] This embodiment proposes a human body perception detection method based on FMCW millimeter wave radar, such as Figure 1 As shown, the following steps are included:

[0073] S1: Use FMCW millimeter-wave radar to collect data on the environment, obtain radar echo signals, perform discrete sampling on the radar echo signals, and obtain several frames of original intermediate frequency signal data;

[0074] S2: Frame processing is performed on each frame of the original intermediate frequency signal data to obtain the adaptive speed amplitude threshold and target speed energy amplitude of the corresponding frame;

[0075] S3: Outputting several human body perception detection results of the radar echo signal according to the adaptive velocity amplitude threshold and target velocity energy amplitude of each frame of original intermediate frequency signal data.

[0076] In the specific implementation process, several frames of original intermediate frequency signal data are first obtained, and then frame processing is performed on each frame of the original intermediate frequency signal data to obtain the adaptive speed amplitude threshold and target speed energy amplitude of the corresponding frame. Then, several human body perception detection results of the radar echo signal are output based on the adaptive speed amplitude threshold and the target speed energy amplitude.

[0077] Example 2

[0078] This embodiment further defines step S2 based on embodiment 1:

[0079] The step S2 comprises:

[0080] S2.1: performing frame reorganization on each frame of the original intermediate frequency signal data to obtain an initial two-dimensional matrix of the corresponding frame, performing mean cancellation on the initial two-dimensional moments to obtain a denoised two-dimensional matrix;

[0081] The initial two-dimensional matrix of the original intermediate frequency signal data of the i-th frame is recorded as R i , the size is M*N, where M is the number of fast time sampling points and N is the number of slow time sampling points. i Sum and average each row of , and obtain the mean matrix C of size M*1 i , specifically:

[0082]

[0083] Among them, m represents the row index, C i [m] means C i The data of the mth index in;

[0084] The matrix R i Each column of data is subtracted from the mean matrix C i [m], obtain the denoised two-dimensional matrix:

[0085] R i1 [m,n]=R i [m,n]-C i [m], m=1, 2,…,M, n=1, 2,…,N

[0086] Among them, R i1 The two-dimensional matrix representing the denoised data of the original IF signal of the i-th frame, where n represents the column index.

[0087] S2.2: Process the denoised two-dimensional matrix to obtain a distance information matrix, and then perform non-correlated accumulation calculation on the distance information matrix to obtain a target range gate;

[0088] The denoised two-dimensional matrix is ​​subjected to distance dimension fast Fourier transform along the fast time axis to obtain the distance transformation matrix, and then the distance transformation matrix is ​​processed by adding Hanning window to obtain the distance information matrix.

[0089] S2.3: Perform static clutter filtering on the distance information matrix to obtain a de-cluttered distance information matrix, and obtain an adaptive velocity amplitude threshold and a target velocity energy amplitude based on the de-cluttered distance information matrix and the target range gate.

[0090] The clutter-free range information matrix is ​​subjected to velocity-dimensional fast Fourier transform and filtering along the slow time axis to obtain the range-Doppler matrix.

[0091] Perform unit-averaged constant false alarm target detection on the range-Doppler matrix along the slow time axis to obtain the adaptive velocity amplitude threshold, specifically:

[0092] Determine the size of the training area and protection area for each row of data in the range Doppler matrix, and set the length of the training unit in the training area to N train =4, the protection unit length is N guard =8;

[0093] Calculate the average signal strength of the training unit as the clutter noise power statistic Z of the unit to be detected;

[0094] Calculate the adaptive speed amplitude threshold T. The specific calculation formula is:

[0095]

[0096] Among them, P fa =1e -6 represents the false alarm probability, N train represents the length of the training unit, and Z represents the average signal strength of the training unit.

[0097] The target range gate is used to extract data from the range Doppler matrix to obtain the target velocity energy amplitude.

[0098] Example 3

[0099] This embodiment further defines the following on the basis of embodiment 2:

[0100] The obtaining of several frames of original intermediate frequency signal data specifically involves discretely sampling the latest radar echo signal of the FMCW millimeter wave radar acquisition environment to obtain the latest several frames of original intermediate frequency signal.

[0101] The step S3 comprises:

[0102] S3.1: Determine whether the target velocity energy amplitude of the first frame of the original intermediate frequency signal data is greater than an adaptive velocity amplitude threshold. If so, the human perception detection result for the first frame is that the target is moving with large amplitude. If so, the human perception detection result for the first frame is that the target is not moving with large amplitude.

[0103] S3.2: Let i = 2;

[0104] S3.3: Based on the human perception detection result of the previous frame of raw IF signal data, use different strategies to detect the raw IF signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large-scale movements, execute step S3.4; if the human perception detection result of the i-1th frame is a human target without large-scale movements, execute step S3.6;

[0105] S3.4: Determine whether the target velocity energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive velocity amplitude threshold. If it is greater than the threshold, the human perception detection result of the i-th frame is a human target with large-amplitude movements. If it is less than the threshold, further determine whether i+L-1>I, where L is the human vital sign detection interval and I is the number of frames of the original intermediate frequency signal data. If so, the human perception detection result of the i-th frame is no human target with large-amplitude movements, and execute step S3.7. Otherwise, extract human vital sign information from the target phase information of the original intermediate frequency signal data of L frames, from the i-th frame to the i+L-1-th frame, and perform vital sign detection and judgment based on the human vital sign information. If it is determined that there are human vital signs, the human perception detection result of the i+L-1-th frame is a stationary human target. If it is determined that there are no human vital signs, the human perception detection result of the i+L-1-th frame is no human target.

[0106] S3.5: Execute i=i+L and determine if i>I. If so, proceed to step S3.9; otherwise, proceed to step S3.8.

[0107] S3.6: Determine whether the target velocity energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive velocity amplitude threshold. If so, the human perception detection result of the i-th frame is a human target with large-amplitude motion. If so, the human perception detection result of the i-th frame is a human target with no large-amplitude motion.

[0108] S3.7: Execute i=i+1 and determine if i>I. If so, proceed to step S3.9. Otherwise, further determine if i>L. If so, proceed to step S3.8. Otherwise, return to step S3.3.

[0109] S3.8: Based on the human perception detection result of the previous frame of raw IF signal data, use different strategies to detect the raw IF signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large movements or a stationary human target, return to step S3.4. If the human perception detection result of the i-1th frame is a human target with no large movements or a non-human target, return to step S3.6.

[0110] like Figure 2 The figure shows a flow chart for different detection strategies. Depending on the human perception detection result of the previous frame, different detection strategies are used: If the human perception detection result of the previous frame indicates a human target with large movements or a stationary human target, the target velocity energy amplitude in the current frame is determined to be greater than the adaptive velocity amplitude threshold. If so, the target is determined to be a human target with large movements. Otherwise, human vital signs detection is performed to obtain the target's breathing and heartbeat signals, and these signals are analyzed to determine whether the target is a human target. If the human perception detection result of the previous frame indicates a human target without large movements or a non-human target, the target velocity energy amplitude in the current frame is determined to be greater than the adaptive velocity amplitude threshold. If so, the target is determined to be a human target with large movements; otherwise, the target is determined to be a non-large movements human target. The human perception detection result of the first frame can only be a human target with large movements or a human target without large movements.

[0111] S3.9: Output several human body perception detection results of the radar echo signal.

[0112] In S3.4, the human vital sign information is extracted from the target phase information of the original intermediate frequency signal data of the i-th frame to the i+L-1-th frame (a total of L frames), and the vital sign detection is performed based on the human vital sign information to determine:

[0113] Get the discrete sequence Phi of the target chest phase information from the i-th frame to the i+L-1-th frame, a total of L frames of original intermediate frequency signal data.

[0114] Phi=[Data i ,Data i+1 ,…,Data i+L-1 ]

[0115] Among them, Data i Indicates the discrete points containing the target chest phase information in the original intermediate frequency signal data of the i-th frame;

[0116] Target phase information, extract L phase discrete points as human vital sign information, and make a judgment based on the human vital sign information. If it is judged that there is human vital sign, the human perception detection result of the i+L-1th frame is a stationary human target. If there is no human vital sign, the human perception detection result of the i+L-1th frame is a non-human target.

[0117] Perform phase inverse tangent on Phi to extract the corresponding target phase signal, and then perform phase unwrapping on the target phase signal to obtain the phase unwrapping signal:

[0118]

[0119] Among them, φ unwrap (i) represents the target phase unwrapping signal of the original intermediate frequency signal data of the i-th frame, and φ(i) represents the target phase signal of the original intermediate frequency signal data of the i-th frame;

[0120] Perform phase difference processing on the target phase dewrapping signal to obtain the target vital sign signal:

[0121] φ diff (i) = φ unwrap (i+1)-φ unwrap (i),i=1,2,…,L-2

[0122] Among them, φ diff (i) represents the target vital sign signal of the original intermediate frequency signal data of the i-th frame;

[0123] A smoothing algorithm is used to smooth the target vital sign signal of the original intermediate frequency signal data from the i-th frame to the i+L-1-th frame (a total of L frames) to obtain the target vital sign smoothed signal. A first elliptical bandpass filter and a second elliptical bandpass filter are designed, and the target vital sign smoothed signal is input into the first elliptical bandpass filter and the second elliptical bandpass filter respectively to obtain the corresponding target breathing signal and heartbeat signal. The target breathing signal and heartbeat signal are detected based on the detection to determine whether the target is a human target.

[0124] The design parameters of the first elliptical bandpass filter and the second elliptical bandpass filter are: passband frequency is 0.2Hz-0.6Hz, stopband boundary frequency is 0.1Hz-0.7Hz, passband ripple is 0.2dB, and stopband minimum attenuation is 30dB.

[0125] The design parameters of the second elliptical bandpass filter are: passband frequency of 0.8Hz-2.0Hz, stopband boundary frequency of 0.7Hz-2.1Hz, passband ripple of 0.2dB, and stopband minimum attenuation of 30dB.

[0126] The method of judging whether the target is a human target based on the target breathing signal and heartbeat signal detection includes:

[0127] Perform fast Fourier transform on the target respiratory signal and heartbeat signal to obtain the target respiratory frequency domain signal and heartbeat frequency domain signal. Calculate the spectrum amplitude of the target respiratory frequency domain signal and heartbeat frequency domain signal respectively, and set the judgment conditions 1 to 4:

[0128] Condition 1: The peak value of the spectrum amplitude of the target respiratory frequency domain signal is greater than the spectrum amplitude threshold of the target respiratory frequency domain signal;

[0129] Condition 2: The peak amplitude position of the target respiratory frequency domain signal is in the range of 0.2Hz-0.6Hz;

[0130] Condition 3: The peak value of the spectrum amplitude of the target heartbeat frequency domain signal is greater than the spectrum amplitude threshold of the target heartbeat frequency domain signal;

[0131] Condition 4: The peak amplitude position of the target heartbeat frequency domain signal is in the range of 0.8 Hz to 2.0 Hz.

[0132] If conditions 1, 2, 3, and 4 are all met, the target is judged to be a stationary human target; otherwise, the target is judged to be a non-human target.

[0133] Example 4

[0134] This embodiment further limits I=129 and L=128 based on Embodiment 3.

[0135] A total of I frames of original intermediate frequency signal data are acquired, and frame processing is performed on each frame of the original intermediate frequency signal data to obtain an adaptive speed amplitude threshold and a target speed energy amplitude of the corresponding frame.

[0136] The target velocity energy amplitude of the original intermediate frequency signal data of the first frame is greater than the adaptive velocity amplitude threshold, so the human perception detection result of the first frame is a human target with large-amplitude movement;

[0137] The target velocity energy amplitude of the second frame of the original intermediate frequency signal data is less than the adaptive velocity amplitude threshold. The human vital sign information is extracted from the target phase information of the 128 frames of original intermediate frequency signal data from the second frame to the 129th frame. It is determined that there are human vital signs. The human perception detection result of the 129th frame is a stationary human target.

[0138] Outputs two human body perception detection results of radar echo signals.

[0139] The same or similar reference numerals correspond to the same or similar components;

[0140] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0141] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A human body perception detection method based on FMCW millimeter wave radar, characterized in that: The following steps are involved: S1: Use FMCW millimeter-wave radar to collect data on the environment, obtain radar echo signals, perform discrete sampling on the radar echo signals, and obtain several frames of original intermediate frequency signal data; S2: Frame processing is performed on each frame of the original intermediate frequency signal data to obtain the adaptive speed amplitude threshold and target speed energy amplitude of the corresponding frame; S3: Outputting several human body perception detection results of the radar echo signal based on the adaptive velocity amplitude threshold and target velocity energy amplitude of each frame of raw intermediate frequency signal data; The S2 includes: S2.1: performing frame reorganization on each frame of the original intermediate frequency signal data to obtain an initial two-dimensional matrix of the corresponding frame, performing mean cancellation on the initial two-dimensional moments to obtain a denoised two-dimensional matrix; S2.2: Process the denoised two-dimensional matrix to obtain a distance information matrix, and then perform non-correlated accumulation calculation on the distance information matrix to obtain a target range gate; S2.3: Perform static clutter filtering on the range information matrix to obtain a de-cluttered range information matrix, and obtain an adaptive velocity amplitude threshold and a target velocity energy amplitude based on the de-cluttered range information matrix and the target range gate; In S2.3, obtaining the adaptive velocity amplitude threshold and the target velocity energy amplitude according to the clutter removal range information matrix and the target range gate includes: The clutter-free range information matrix is ​​subjected to velocity-dimensional fast Fourier transform and filtering along the slow time axis to obtain the range-Doppler matrix. Perform unit-averaged constant false alarm target detection on the range-Doppler matrix along the slow time axis to obtain the adaptive velocity amplitude threshold, specifically: Determine the size of the training area and protection area for each row of data in the range Doppler matrix, and set the length of the training unit in the training area to N train =4, the protection unit length is N guard =8; Calculate the average signal strength of the training unit as the clutter noise power statistic Z of the unit to be detected; Calculate the adaptive speed amplitude threshold T. The specific calculation formula is: Among them, P fa =1e -6 represents the false alarm probability, N train represents the length of the training unit, and Z represents the average signal strength of the training unit; The target range gate is used to extract data from the range Doppler matrix to obtain the target velocity energy amplitude; The S3 includes: S3.1: Determine whether the target velocity energy amplitude of the first frame of the original intermediate frequency signal data is greater than an adaptive velocity amplitude threshold. If so, the human perception detection result for the first frame is that the target is moving with large amplitude. If so, the human perception detection result for the first frame is that the target is not moving with large amplitude. S3.2: Let i = 2; S3.3: Based on the human perception detection result of the previous frame of raw IF signal data, use different strategies to detect the raw IF signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large-scale movements, execute step S3.4; if the human perception detection result of the i-1th frame is a human target without large-scale movements, execute step S3.6; S3.4: Determine whether the target velocity energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive velocity amplitude threshold. If it is greater than the threshold, the human perception detection result of the i-th frame is a human target with large-amplitude movements. If it is less than the threshold, further determine whether i+L-1>I, where L is the human vital sign detection interval and I is the number of frames of the original intermediate frequency signal data. If so, the human perception detection result of the i-th frame is no human target with large-amplitude movements, and execute step S3.

7. Otherwise, extract human vital sign information from the target phase information of the original intermediate frequency signal data of L frames, from the i-th frame to the i+L-1-th frame, and perform vital sign detection and judgment based on the human vital sign information. If it is determined that there are human vital signs, the human perception detection result of the i+L-1-th frame is a stationary human target. If it is determined that there are no human vital signs, the human perception detection result of the i+L-1-th frame is no human target. S3.5: Execute i=i+L and determine if i>I. If so, proceed to step S3.9; otherwise, proceed to step S3.

8. S3.6: Determine whether the target velocity energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive velocity amplitude threshold. If so, the human perception detection result of the i-th frame is a human target with large-amplitude motion. If so, the human perception detection result of the i-th frame is a human target with no large-amplitude motion. S3.7: Execute i=i+1 and determine if i>I. If so, proceed to step S3.

9. Otherwise, further determine if i>L. If so, proceed to step S3.

8. Otherwise, return to step S3.

3. S3.8: Based on the human perception detection result of the previous frame of raw IF signal data, use different strategies to detect the raw IF signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large movements or a stationary human target, return to step S3.

4. If the human perception detection result of the i-1th frame is a human target with no large movements or a non-human target, return to step S3.

6. S3.9: Output several human body perception detection results of the radar echo signal.

2. The human body perception detection method based on FMCW millimeter wave radar according to claim 1, characterized in that: In S2.1, performing mean cancellation on the initial two-dimensional moments to obtain a denoised two-dimensional matrix includes: The initial two-dimensional matrix of the original intermediate frequency signal data of the i-th frame is recorded as R i , the size is M*N, where M is the number of fast time sampling points and N is the number of slow time sampling points. i Sum and average each row of , and obtain the mean matrix C of size M*1 i , specifically: Among them, m represents the row index, C i [m] means C i The data of the mth index in; The matrix R i Each column of data is subtracted from the mean matrix C i [m], obtain the denoised two-dimensional matrix: R i1 [m,n]=R i [m,n]-C i [m],m=1,2,…,M,n=1,2,…,N Among them, R i1 The two-dimensional matrix representing the denoised data of the original IF signal of the i-th frame, where n represents the column index.

3. The human body perception detection method based on FMCW millimeter wave radar according to claim 1, characterized in that: In S2.2, processing the denoised two-dimensional matrix to obtain a distance information matrix includes: The denoised two-dimensional matrix is ​​subjected to distance dimension fast Fourier transform along the fast time axis to obtain the distance transformation matrix, and then the distance transformation matrix is ​​processed by adding Hanning window to obtain the distance information matrix.

4. The human body perception detection method based on FMCW millimeter wave radar according to claim 1, characterized in that: In S3.4, extracting human vital sign information from target phase information of L frames of original intermediate frequency signal data from the i-th frame to the i+L-1-th frame, and performing vital sign detection and judgment based on the human vital sign information includes: Get the discrete sequence Phi of the target chest phase information from the i-th frame to the i+L-1-th frame, a total of L frames of original intermediate frequency signal data. Phi=[Data i ,Data i+1 ,…,Data i+L-1 ] Among them, Data i Indicates the discrete points containing the target chest phase information in the original intermediate frequency signal data of the i-th frame; Perform phase inverse tangent on Phi to extract the corresponding target phase signal, and then perform phase unwrapping on the target phase signal to obtain the phase unwrapping signal: Among them, φ unwrap (i) represents the target phase unwrapping signal of the original intermediate frequency signal data of the i-th frame, and φ(i) represents the target phase signal of the original intermediate frequency signal data of the i-th frame; Perform phase difference processing on the target phase dewrapping signal to obtain the target vital sign signal: φ diff (i)=φ unwrap (i+1)-φ unwrap (i),i=1,2,…,L-2 Among them, φ diff (i) represents the target vital sign signal of the original intermediate frequency signal data of the i-th frame; A smoothing algorithm is used to smooth the target vital sign signal of the original intermediate frequency signal data from the i-th frame to the i+L-1-th frame (a total of L frames) to obtain the target vital sign smoothed signal. A first elliptical bandpass filter and a second elliptical bandpass filter are designed, and the target vital sign smoothed signal is input into the first elliptical bandpass filter and the second elliptical bandpass filter respectively to obtain the corresponding target respiratory signal and heartbeat signal. The target respiratory signal and heartbeat signal are detected based on the detection to determine whether the target is a human target.

5. The human body perception detection method based on FMCW millimeter wave radar according to claim 4, characterized in that: The design parameters of the first elliptical bandpass filter and the second elliptical bandpass filter include: passband frequency of 0.2Hz-0.6Hz, stopband boundary frequency of 0.1Hz-0.7Hz, passband ripple of 0.2dB, and stopband minimum attenuation of 30dB.

6. The human body perception detection method based on FMCW millimeter wave radar according to claim 4, characterized in that: The design parameters of the second elliptical bandpass filter include: passband frequency of 0.8Hz-2.0Hz, stopband boundary frequency of 0.7Hz-2.1Hz, passband ripple of 0.2dB, and stopband minimum attenuation of 30dB.

7. The human body perception detection method based on FMCW millimeter wave radar according to claim 4, characterized in that: The method of judging whether the target is a human target based on the target breathing signal and heartbeat signal detection includes: Perform fast Fourier transform on the target respiratory signal and heartbeat signal to obtain the target respiratory frequency domain signal and heartbeat frequency domain signal. Calculate the spectrum amplitude of the target respiratory frequency domain signal and heartbeat frequency domain signal respectively, and set the judgment conditions 1 to 4: Condition 1: The peak value of the spectrum amplitude of the target respiratory frequency domain signal is greater than the spectrum amplitude threshold of the target respiratory frequency domain signal; Condition 2: The peak amplitude position of the target respiratory frequency domain signal is in the range of 0.2Hz-0.6Hz; Condition 3: The peak value of the spectrum amplitude of the target heartbeat frequency domain signal is greater than the spectrum amplitude threshold of the target heartbeat frequency domain signal; Condition 4: The peak amplitude position of the target heartbeat frequency domain signal is in the range of 0.8 Hz to 2.0 Hz. If conditions 1, 2, 3, and 4 are all met, the target is judged to be a stationary human target; otherwise, the target is judged to be a non-human target.

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