Human body sensing detection method based on FMCW millimeter wave radar

Abstract

The invention discloses a human body sensing detection method based on an FMCW millimeter wave radar, and the method comprises the steps: carrying out the data collection and processing of an environment through employing the FMCW millimeter wave radar, obtaining a plurality of frames of original intermediate frequency signal data, carrying out the frame processing, and obtaining a self-adaptive speed amplitude threshold value and a target speed energy amplitude of a corresponding frame, detecting by using different strategies according to the human body sensing detection result of the previous frame: if the human body sensing detection result of the previous frame is a large-amplitude motion human body target or a static human body target, judging whether the speed energy amplitude of the current frame target is greater than an adaptive speed amplitude threshold, and if so, executing the step 2; if yes, judging that the target is a human body target with large-amplitude actions; otherwise, human body vital sign detection is carried out, target respiration signals and heartbeat signals are obtained, and whether the signals are human body targets or not is analyzed and judged; if the human body sensing detection result of the previous frame is a human body target without large-amplitude action or a non-human body target, judging whether the speed energy amplitude of the target of the current frame is greater than an adaptive speed amplitude threshold, and if so, judging that the target of the current frame is a human body target with large-amplitude action; otherwise, judging the target as a non-large-amplitude motion human body target. According to the invention, static human body targets and human body targets with large-amplitude actions can be detected, and more accurate human body sensing detection in different scenes can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, 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 working principle is that the RF front end of the radar system sends out electromagnetic wave signals through the transmitting antenna. The electromagnetic wave signals are reflected by the target object and captured by the receiving antenna. Digital signal processing is performed to obtain target information and complete target detection. In the past, sensors only needed to determine whether a target existed for target detection. However, with the rapid development of science and technology, in order to meet actual needs, it is also necessary to obtain various information such as the distance, speed, angle, etc. of the target at the same time to accurately detect and identify the target.

[0003] FMCW (Frequency Modulated Continuous Wave) millimeter wave radar has obvious advantages in target detection. It uses the unique frequency variation characteristics of electromagnetic waves to obtain target information by transmitting chirp signals whose frequency changes linearly with time. It has long-distance detection capabilities and high resolution, and realizes high-precision target detection and tracking capabilities in complex environments. In addition, FMCW millimeter wave radar is very sensitive to tiny movements and can detect tiny amplitude movements of the human chest caused by breathing and heartbeat, so it is also suitable for human vital signs detection.

[0004] However, the existing technical methods have certain limitations. The FFT algorithm based on frequency domain analysis extracts the frequency characteristics of the target in human target detection to obtain the distance and speed information of the target. Although it can identify human targets with large movements, it is not enough to effectively identify stationary human targets, and may face the problems of false detection and missed detection of human targets. The human vital signs detection method can effectively identify stationary human targets, but it takes a certain amount of time to collect the phase change information of the chest cavity of the human target, resulting in a long reaction time for human target detection. In addition, when the human target has large movements, it is difficult for the radar to extract the tiny amplitude of the human chest cavity, and the detection effect of this method is not good. At the same time, in general, people have high requirements for the response speed of the radar from unmanned to manned, and low requirements for the response speed from manned 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 that the above-mentioned prior art cannot simultaneously detect a stationary human target and a human target 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 solution of the present invention is 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: Perform frame processing on each frame of original intermediate frequency signal data to obtain an adaptive speed amplitude threshold and a 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 speed amplitude threshold and the target speed energy amplitude of each frame of the original intermediate frequency signal data.

[0011] Preferably, the step S2 comprises:

[0012] S2.1: performing frame reorganization on each frame of 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, and obtaining 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 clutter-free distance information matrix, and obtain an adaptive speed amplitude threshold and a target speed energy amplitude according to the clutter-free distance information matrix and the target range gate.

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

[0016] The initial two-dimensional matrix of the original intermediate frequency signal data of the i-th frame is denoted 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 to obtain a 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 A two-dimensional matrix representing the denoised data of the original intermediate frequency 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 a distance transformation matrix, and then the distance transformation matrix is ​​processed by adding a Hanning window to obtain a distance information matrix.

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

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

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

[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, S3 includes:

[0034] S3.1: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the first frame is greater than the adaptive speed amplitude threshold. If it is greater than the threshold, the human body perception detection result of the first frame is a human body target with large-amplitude motion. If it is less than the threshold, the human body perception detection result of the first frame is a human body target without large-amplitude motion.

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

[0036] S3.3: According to the human perception detection result of the previous frame of raw intermediate frequency signal data, different strategies are used to detect the raw intermediate frequency signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large movements, step S3.4 is executed; if the human perception detection result of the i-1th frame is a human target without large movements, step S3.6 is executed;

[0037] S3.4: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive speed 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 that 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 a human target without 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 frame, and perform vital sign detection and judgment based on the human vital sign information. If it is judged that there are human vital signs, the human perception detection result of the i+L-1 frame is a stationary human target. If it is judged that there are no human vital signs, the human perception detection result of the i+L-1 frame is a non-human target.

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

[0039] S3.6: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive speed amplitude threshold. If it is greater than the threshold, the human body perception detection result of the i-th frame is a human body target with large-amplitude motion. If it is less than the threshold, the human body perception detection result of the i-th frame is a human body target without large-amplitude motion.

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

[0041] S3.8: According to the human perception detection result of the previous frame of raw intermediate frequency signal data, different strategies are used to detect the raw intermediate frequency 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, the process returns to step S3.4; if the human perception detection result of the i-1th frame is a human target without large movements or a non-human target, the process returns to step S3.6;

[0042] S3.9: Obtain several human body perception detection results of the radar echo signal.

[0043] Preferably, 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 according to the human vital sign information includes:

[0044] 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.

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

[0046] 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;

[0047] 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:

[0048]

[0049] 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;

[0050] Perform phase difference processing on the target phase unwrapping signal to obtain the 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 signals of L frames of original intermediate frequency signal data from the i-th frame to the i+L-1-th frame to obtain target vital sign smoothed signals. A first elliptical bandpass filter and a second elliptical bandpass filter are designed, and the target vital sign smoothed signals are input into the first elliptical bandpass filter and the second elliptical bandpass filter respectively to obtain corresponding target breathing signals and heartbeat signals. It is determined whether the target is a human target based on the target breathing signals and heartbeat signals.

[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: a passband frequency of 0.8 Hz-2.0 Hz, a stopband boundary frequency of 0.7 Hz-2.1 Hz, a passband ripple of 0.2 dB, and a stopband minimum attenuation of 30 dB.

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

[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 position of the spectrum amplitude of the target respiratory frequency domain signal is in the range of 0.2 Hz-0.6 Hz;

[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 position of the spectrum amplitude of the target heartbeat frequency domain signal is in the range of 0.8 Hz-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 technical solution of the present invention has the following beneficial effects:

[0064] The present invention proposes a human perception detection method based on FMCW millimeter wave radar, which can detect stationary human targets and human targets with large movements, distinguish whether there are human targets with large movements through target speed information, and then combine the target vital signs detection to detect stationary human targets. Different detection strategies are performed on the current frame according to the detection results of the previous frame to achieve rapid judgment from no one to someone, and accurate judgment from someone to no one. 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 methods described in Example 3. DETAILED DESCRIPTION

[0067] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

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

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

[0070] The technical solution of the present invention is further described below in conjunction with 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: Perform frame processing on each frame of original intermediate frequency signal data to obtain an adaptive speed amplitude threshold and a 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 speed amplitude threshold and the target speed energy amplitude of each frame of the 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, and then several personal human body perception detection results of the radar echo signal are output according to 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 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, and obtaining a denoised two-dimensional matrix;

[0081] The initial two-dimensional matrix of the original intermediate frequency signal data of the i-th frame is denoted 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 to obtain a 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 A two-dimensional matrix representing the denoised data of the original intermediate frequency 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 a distance transformation matrix, and then the distance transformation matrix is ​​processed by adding a Hanning window to obtain a distance information matrix.

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

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

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

[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 speed energy amplitude of the original intermediate frequency signal data of the first frame is greater than the adaptive speed amplitude threshold. If it is greater than the threshold, the human body perception detection result of the first frame is a human body target with large-amplitude motion. If it is less than the threshold, the human body perception detection result of the first frame is a human body target without large-amplitude motion.

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

[0104] S3.3: According to the human perception detection result of the previous frame of raw intermediate frequency signal data, different strategies are used to detect the raw intermediate frequency signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large movements, step S3.4 is executed; if the human perception detection result of the i-1th frame is a human target without large movements, step S3.6 is executed;

[0105] S3.4: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive speed 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 that 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 a human target without 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 frame, and perform vital sign detection and judgment based on the human vital sign information. If it is judged that there are human vital signs, the human perception detection result of the i+L-1 frame is a stationary human target. If it is judged that there are no human vital signs, the human perception detection result of the i+L-1 frame is a non-human target.

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

[0107] S3.6: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive speed amplitude threshold. If it is greater than the threshold, the human body perception detection result of the i-th frame is a human body target with large-amplitude motion. If it is less than the threshold, the human body perception detection result of the i-th frame is a human body target without large-amplitude motion.

[0108] S3.7: Execute i=i+1, determine if i>I, if so, execute step S3.9, otherwise, further determine if i>L, if so, execute step S3.8, otherwise, return to step S3.3;

[0109] S3.8: According to the human perception detection result of the previous frame of raw intermediate frequency signal data, different strategies are used to detect the raw intermediate frequency 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, the process returns to step S3.4; if the human perception detection result of the i-1th frame is a human target without large movements or a non-human target, the process returns to step S3.6;

[0110] like Figure 2 As shown in the figure, it is a flow chart of different strategy detection. According to the human perception detection result of the previous frame, different strategies are used for detection: if the human perception detection result of the previous frame is a human target with large movements or a stationary human target, then it is determined whether the speed energy amplitude of the target in the current frame is greater than the adaptive speed amplitude threshold. If it is greater, it is determined to be a human target with large movements; otherwise, human vital signs detection is performed to obtain the target breathing signal and heartbeat signal, and then analyzed to determine whether it is a human target. If the human perception detection result of the previous frame is a human target without large movements or a non-human target, then it is determined whether the speed energy amplitude of the target in the current frame is greater than the adaptive speed amplitude threshold. If it is greater, it is determined to be a human target with large movements; otherwise, it is determined to be a human target without large movements. Among them, 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 (L frames in total), and the vital sign detection is performed according to the human vital sign information as follows:

[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 signs information, and make a judgment based on the human vital signs information. If it is judged that there are human vital signs, the human perception detection result of the i+L-1th frame is a stationary human target. If there are no human vital signs, 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 unwrapping 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 signals of L frames of original intermediate frequency signal data from the i-th frame to the i+L-1-th frame to obtain target vital sign smoothed signals. A first elliptical bandpass filter and a second elliptical bandpass filter are designed, and the target vital sign smoothed signals are input into the first elliptical bandpass filter and the second elliptical bandpass filter respectively to obtain corresponding target breathing signals and heartbeat signals. It is determined whether the target is a human target based on the target breathing signals and heartbeat signals.

[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 is 0.8Hz-2.0Hz, stopband boundary frequency is 0.7Hz-2.1Hz, passband ripple is 0.2dB, and stopband minimum attenuation is 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 position of the spectrum amplitude of the target respiratory frequency domain signal is in the range of 0.2 Hz-0.6 Hz;

[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 position of the spectrum amplitude of the target heartbeat frequency domain signal is in the range of 0.8 Hz-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 on the basis of 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 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 speed energy amplitude of the original intermediate frequency signal data of the first frame is greater than the adaptive speed amplitude threshold, so the human perception detection result of the first frame is a human target with large-amplitude motion;

[0137] The target speed energy amplitude of the second frame of the original intermediate frequency signal data is less than the adaptive speed amplitude threshold. The human vital signs 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, and it is judged that there are human vital signs. Then the human perception detection result of the 129th frame is a stationary human target.

[0138] Outputs 2 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 only used for illustrative purposes and should not be construed as limiting this patent;

[0141] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope 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: Perform frame processing on each frame of original intermediate frequency signal data to obtain an adaptive speed amplitude threshold and a target speed energy amplitude of the corresponding frame; S3: Outputting several human body perception detection results of the radar echo signal according to the adaptive speed amplitude threshold and the target speed energy amplitude of each frame of the original intermediate frequency signal data.

2. The human body perception detection method based on FMCW millimeter wave radar according to claim 1 is characterized in that: The S2 includes: S2.1: performing frame reorganization on each frame of 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, and obtaining 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 distance information matrix to obtain a clutter-free distance information matrix, and obtain an adaptive speed amplitude threshold and a target speed energy amplitude according to the clutter-free distance information matrix and the target range gate.

3. The human body perception detection method based on FMCW millimeter wave radar according to claim 2 is characterized in that: In S2.1, performing mean cancellation on the initial two-dimensional moment 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 denoted 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 to obtain a 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 A two-dimensional matrix representing the denoised data of the original intermediate frequency signal of the i-th frame, where n represents the column index.

4. The human body perception detection method based on FMCW millimeter wave radar according to claim 2 is characterized in that: In S2.2, the processing of 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 a distance transformation matrix, and then the distance transformation matrix is ​​subjected to Hanning window processing to obtain a distance information matrix.

5. The human body perception detection method based on FMCW millimeter wave radar according to claim 2 is characterized in that: In S2.3, obtaining the adaptive speed amplitude threshold and the target speed energy amplitude according to the clutter removal distance information matrix and the target range gate includes: The clutter-free range information matrix is ​​subjected to speed-dimensional fast Fourier transform and filtering along the slow time axis to obtain the range Doppler matrix. Perform unit average constant false alarm target detection on the range Doppler matrix along the slow time axis to obtain the adaptive velocity amplitude threshold, which is: 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.

6. The human body perception detection method based on FMCW millimeter wave radar according to claim 5 is characterized in that: The S3 includes: S3.1: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the first frame is greater than the adaptive speed amplitude threshold. If it is greater than the threshold, the human body perception detection result of the first frame is a human body target with large-amplitude motion. If it is less than the threshold, the human body perception detection result of the first frame is a human body target without large-amplitude motion. S3.2: let i=2; S3.3: According to the human perception detection result of the previous frame of raw intermediate frequency signal data, different strategies are used to detect the raw intermediate frequency signal data of the current frame. If the human perception detection result of the i-1th frame is a human target with large movements, step S3.4 is executed; if the human perception detection result of the i-1th frame is a human target without large movements, step S3.6 is executed; S3.4: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive speed 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 that 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 a human target without 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 frame, and perform vital sign detection and judgment based on the human vital sign information. If it is judged that there are human vital signs, the human perception detection result of the i+L-1 frame is a stationary human target. If it is judged that there are no human vital signs, the human perception detection result of the i+L-1 frame is a non-human target. S3.5: Execute i=i+L, and determine if i>I. If so, execute step S3.9; otherwise, execute step S3.8; S3.6: Determine whether the target speed energy amplitude of the original intermediate frequency signal data of the i-th frame is greater than the adaptive speed amplitude threshold. If it is greater than the threshold, the human body perception detection result of the i-th frame is a human body target with large-amplitude motion. If it is less than the threshold, the human body perception detection result of the i-th frame is a human body target without large-amplitude motion. S3.7: Execute i=i+1, determine if i>I, if so, execute step S3.9, otherwise, further determine if i>L, if so, execute step S3.8, otherwise, return to step S3.3; S3.8: According to the human perception detection result of the previous frame of raw intermediate frequency signal data, different strategies are used to detect the raw intermediate frequency 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, the process returns to step S3.4; if the human perception detection result of the i-1th frame is a human target without large movements or a non-human target, the process returns to step S3.6; S3.9: Output several human body perception detection results of the radar echo signal.

7. The human body perception detection method based on FMCW millimeter wave radar according to claim 6 is 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 according to 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 unwrapping 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 signals of L frames of original intermediate frequency signal data from the i-th frame to the i+L-1-th frame to obtain target vital sign smoothed signals, and a first elliptical bandpass filter and a second elliptical bandpass filter are designed. The target vital sign smoothed signals are respectively input into the first elliptical bandpass filter and the second elliptical bandpass filter to obtain corresponding target breathing signals and heartbeat signals, and it is determined whether the target is a human target based on the target breathing signal and heartbeat signal detection.

8. The human body perception detection method based on FMCW millimeter wave radar according to claim 7 is 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.

9. The human body perception detection method based on FMCW millimeter wave radar according to claim 7, 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.

10. The human body perception detection method based on FMCW millimeter wave radar according to claim 7, 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 position of the spectrum amplitude of the target respiratory frequency domain signal is in the range of 0.2 Hz-0.6 Hz; 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 position of the spectrum amplitude of the target heartbeat frequency domain signal is in the range of 0.8 Hz-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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