Method for Detecting Vital Signs Based on Millimeter-Wave Radar

Abstract

The present invention discloses a method for detecting vital signs based on a millimeter-wave radar. The method includes the steps of transmitting a frequency-modulated continuous wave (FMCW), mixing the echo signal to obtain original data, and forming the original data into a first matrix including a fast-time dimension and a slow-time dimension; performing a one-dimensional fast Fourier transform on the first matrix to obtain a corresponding second matrix; then performing a stationary filtering operation on the second matrix to obtain a fourth matrix; performing an unwrapping operation on the distance cells where a human target is judged to exist in the fourth matrix; judging the vital signs of the human target from the unwrapped phase signal; extracting corresponding chest wall displacement signals from the distance cells of the human target with vital signs; and reconstructing the chest wall displacement-time signal to obtain the accurate vital signs of the human target. Compared with the prior art, the present invention improves the accuracy of monitoring the human heart rate and respiration, and better reduces the computational amount in the detection process, thereby enhancing the ability of rapid response.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar equipment, and particularly relates to a method for detecting vital signs based on a millimeter-wave radar. Background Art

[0002] Currently, among the vital sign detection technologies for daily physical examinations, medical treatment, sleep monitoring of sleep apnea patients, early warning of fatigue driving, post-disaster rescue, and other fields in the prior art, there is a millimeter-wave radar. The existing methods for detecting vital signs using a millimeter-wave radar generally include three major processes: emitting radar waves to detect distance, judging whether it is a human body according to the distance-velocity / angle spectrum of the radar echo, and extracting the heartbeat and respiration from the echo of the human body.

[0003] However, for the existing methods of detecting vital signs using a millimeter-wave radar, the detection accuracy is low, and there is no good denoising, signal screening, and signal reconstruction processing for the radar echo, resulting in frequent missed judgments or misjudgments, and it is impossible to efficiently characterize the strength state of the vital signs of the detected human target. On the other hand, the algorithms in the existing detection methods for extracting respiration and heartbeat signs have an excessive amount of calculation, making it difficult to respond quickly and not conducive to improving the detection accuracy. Summary of the Invention

[0004] In order to overcome one or more defects and deficiencies existing in the prior art, the purpose of the present invention is to provide a method for detecting vital signs based on a millimeter-wave radar, which is used to improve the detection accuracy of the millimeter-wave radar device during the vital sign detection process.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions.

[0006] A method for detecting vital signs based on a millimeter-wave radar includes the following steps:

[0007] Use the antenna of the millimeter-wave radar to transmit a frequency-modulated continuous wave FMCW, then the millimeter-wave radar obtains the echo, mixes the echo signal to obtain the original data, and respectively forms a first matrix including the fast time dimension and the slow time dimension for each frame of the original data;

[0008] Perform a one-dimensional fast Fourier transform on the first matrix to separate the echo signals at different distances to obtain a corresponding second matrix; then perform an operation to filter out stationary clutter on the second matrix. First, use a window to convert the second matrix into a third matrix, and then use the mean value to perform an operation on the third matrix to obtain a fourth matrix, thereby completing the stationary filtering operation;

[0009] Perform a one-dimensional constant false alarm rate detection CFAR operation on the fourth matrix to determine the range cells where human targets exist; then extract the phase signals from the range cells where human targets exist and use the extended-DACM algorithm to perform an unwrapping operation to obtain the unwrapped phase signals.

[0010] Judge the vital signs of the human target from the unwrapped phase signals; extract the corresponding chest wall displacement signals from the range cells of the human target with vital signs, and then use the iterative adjusted time window algorithm to adjust to obtain the adjusted chest wall displacement signals.

[0011] Reconstruct the chest wall displacement-time signal from the adjusted chest wall displacement signals, and then obtain the accurate vital signs of the human target from the reconstructed chest wall displacement-time signal.

[0012] Preferably, the specific process of transmitting a frequency-modulated continuous wave FMCW is as follows:

[0013] Use a single-transmitter single-receiver or single-transmitter multi-receiver millimeter-wave radar to transmit a frequency-modulated continuous wave FMCW into the space where vital sign detection is required. The transmitted signal x(t) within a single chirp time is as follows: T (t) waveform is as follows:

[0014]

[0015] where t represents time, A is the amplitude of the transmitted signal, f s is the starting frequency of the transmitted signal, B is the working bandwidth of the transmitted signal, T c is the duration occupied by a single chirp in the transmitted signal, and φ(t) is the phase noise of the frequency-modulated continuous wave.

[0016] Furthermore, the process of mixing the echo signal to obtain the original data is as follows:

[0017] The obtained echo signal x R (t) is as shown in the following formula:

[0018] x R (t) = αx T (t - t d )

[0019] Mix the echo signal x R (t) and the transmitted signal x T (t) of the frequency-modulated continuous wave to obtain the required intermediate-frequency signal x IF (t) The process is as shown in the following formula:

[0020]

[0021] where c is the speed of light, λ is the wavelength, A Ris the received signal power when the antenna of the millimeter-wave radar receives the echo;

[0022] Perform analog-to-digital conversion on the intermediate-frequency signal x IF (t) to obtain the original data; each frame of the original data is respectively composed into a first matrix including the fast-time dimension and the slow-time dimension, denoted as M[m, n], where m represents the number of sampling points corresponding to a single chirp in the fast-time dimension, and n represents the total number of chirps corresponding to each frame of the original data in the slow-time dimension.

[0023] Further, the process of performing one-dimensional fast Fourier transform on the first matrix is as follows:

[0024] Perform fast Fourier transform on the first matrix M[m, n] column by column, and denote the matrix obtained after performing fast Fourier transform on the first matrix M[m, n] as the second matrix RP[m, n].

[0025] Further, the process of filtering out stationary clutter from the second matrix is as follows:

[0026] Calculate the mean value of each row in the second matrix RP[m, n], and then take out the mean values of each row to form a first vector. The dimension of each first vector corresponds to m of each frame of the second matrix RP[m, n]; then use a window to filter out stationary clutter; set the window size to k, and the window acts on k first vectors to form a third matrix of m*k dimensions; subtract the mean value of all the numerical values in each row from each numerical value in each row of the third matrix to obtain the fourth matrix RS[m, k] after filtering out stationary clutter.

[0027] Further, the process of performing unwrapping operation on the distance cell with a human target using the extended-DACM algorithm is as follows:

[0028] Extract the two paths of signals, the imaginary part I(t) and the real part Q(t), from the fourth matrix RS[m,k] corresponding to the distance cell, and use the extended-DACM algorithm to perform phase unwrapping on the two paths of signals, the imaginary part I(t) and the real part Q(t), respectively, to obtain the phase signal without phase ambiguity.

[0029] Further, the process of detecting the vital signs of a human target from the unwrapped phase signal is as follows:

[0030] Select the total number of frames s for judging live detection from the unwrapped phase signal, and form a vector matrix H of 1*s dimensions with the frequency values of the phase signals corresponding to these s frames after unwrapping;

[0031] Perform a fast Fourier transform on the vector matrix H to obtain the fifth matrix HF[1, s], calculate the ratio of the spectral energy within the set frequency range to the total energy of the entire frequency band, and determine whether the ratio of the spectral energy within the set frequency range to the total energy of the entire frequency band exceeds a preset ratio threshold. The calculation formula for the ratio of the spectral energy within the set frequency range to the total energy of the entire frequency band is as follows:

[0032]

[0033] where i and j each represent the frame number, a corresponds to the row number where a human target exists in the fourth matrix RS[m, k] after one-dimensional constant false alarm detection CFAR, and n here is the number of points for fast Fourier transform within the set frequency range;

[0034] Calculate the variance of the vector matrix H and determine whether the variance exceeds a preset variance threshold;

[0035] If both the ratio of the spectral energy within the set frequency range to the total energy of the entire frequency band and the variance of the vector matrix H exceed the ratio threshold and the variance threshold, it is determined that the human target has vital signs, and then the range cell of the human target with vital signs is extracted.

[0036] Furthermore, the process of using the iterative adjusted time window algorithm to adjust the chest wall displacement signal is as follows:

[0037] The phase signal after unwrapping Calculate the chest wall displacement signal R[n], and the calculation formula is as follows:

[0038]

[0039] Extract a segment of data with a set duration from the chest wall displacement signal R[n], perform a fast Fourier transform with the time window N, and use the frequency of the frequency point where the maximum amplitude of the chest wall displacement spectrum obtained after the fast Fourier transform is located as the respiratory dominant frequency f b1 , and use the frequency of the point where the maximum amplitude of the chest wall displacement spectrum obtained after the fast Fourier transform is located within the set frequency range as the heartbeat dominant frequency f h ;

[0040] Obtain the respiratory dominant frequency f b1 , the respiratory dominant frequency amplitude |A1|, and the second harmonic frequency f b2 ;

[0041] Determine whether the respiratory dominant frequency f b1 is half of the second harmonic frequency f b2 of respiration, and at the same time determine whether the respiratory dominant frequency amplitude is greater than the respiratory dominant frequency amplitude in the previous iteration, that is, determine whether |A1| > |A1′|; if both are not satisfied and |A1|>|A1′|, then adjust the number of points in the time window N and re-perform the fast Fourier transform until both And |A1|>|A1′|.

[0042] Furthermore, the process of reconstructing the chest wall displacement-time signal is as follows:

[0043] The chest wall displacement-time signal caused by breathing is expressed as follows:

[0044]

[0045] Among them, ω1, ω2, and ω3 are the main frequency, double frequency, and triple frequency of breathing respectively, and A1, A2, and A3 are the amplitudes corresponding to the main frequency, double frequency, and triple frequency of breathing respectively;

[0046] For the adjusted chest wall displacement spectrum, there are a priori conditions: the peak amplitude of the main respiratory frequency is approximately 3.5 times the peak amplitude of its second frequency, and the peak amplitude of the main respiratory frequency is approximately 10 times the peak amplitude of its third frequency. Based on the a priori conditions, the root of the following formula is obtained:

[0047] A3x 3 +A2x 2 +A1x-H(t)=0

[0048] Solve the above equation for the root that meets the prior conditions, substitute the root into the above equation to obtain the reconstructed chest wall displacement-time signal.

[0049] Furthermore, the process of obtaining vital signs from the reconstructed chest wall displacement-time signal is as follows:

[0050] In the reconstructed chest wall displacement-time signal, the frequency at which the amplitude is the largest is set as the respiratory frequency f b , the maximum amplitude within the set frequency range is the heart rate f h ;

[0051] Calculate the number of respirations and heartbeats per minute respectively to obtain the vital signs of the corresponding human target, as shown in the following formula:

[0052] HB=60×f b

[0053] HB=60×f h

[0054] Where HB is the number of breaths per minute and HR is the number of heart beats per minute.

[0055] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0056] Compared with the prior art, the present invention performs good denoising, signal screening, and signal reconstruction on radar echoes, and efficiently characterizes the strength state of the vital signs of the detected human target, avoiding missed or misjudged detections and improving the detection accuracy; the related computation amount of the present invention is small, avoiding the problem that the prior art is difficult to respond quickly due to a large computation amount in the process of extracting respiratory and heartbeat signs, and increasing the reliability of measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a schematic diagram of the general process of one of the vital sign detection methods based on millimeter-wave radar according to the present invention;

[0058] Figure 2 is Figure 1 the original chest wall displacement spectrogram in

[0059] Figure 3 is Figure 1 the chest wall displacement spectrogram after iterative adjustment of the time window in

[0060] Figure 4 is Figure 1 the reconstructed chest wall displacement spectrogram in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and their embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] Embodiment

[0063] As Figures 1 to 4 shown, a vital sign detection method based on millimeter-wave radar in this embodiment includes the following steps:

[0064] S1. Use the antenna of the millimeter-wave radar to transmit a frequency-modulated continuous wave FMCW, and then the millimeter-wave radar obtains the echo;

[0065] In this embodiment, it is preferably set that when the antenna of the millimeter-wave radar is facing the space area to be detected, the antenna of the millimeter-wave radar and the chest wall of the human target to be measured are on the same horizontal plane. The distance between the antenna of the millimeter-wave radar and the human body in this embodiment is most preferably about one meter, and the radar antenna does not move during signal transmission and reception;

[0066] In this embodiment, when transmitting the frequency-modulated continuous wave FMCW, it is preferably a millimeter-wave radar with single transmit and single receive or single transmit and multiple receive, and transmit the frequency-modulated continuous wave FMCW into the space where vital sign detection is required. The x T (t) waveform during a single chirp time of the transmitted signal is as follows:

[0067]

[0068] Among them, t represents the moment, A is the amplitude of the transmitted signal, f s is the starting frequency of the transmitted signal, B is the operating bandwidth of the transmitted signal, T c is the duration occupied by a single chirp in the transmitted signal, and φ(t) is the phase noise of the frequency-modulated continuous wave;

[0069] S2. Preprocess the echo obtained in step S1; the specific process is as follows:

[0070] First, construct the echo signal; the obtained echo signal x R (t) is as shown in the following formula:

[0071] x R (t) = αx T (t - t d )

[0072] The echo signal x R (t) compared with the signal x T (t) of the transmitted frequency-modulated continuous wave will produce differences in the amplitude change scale α and the time shift t d The time shift t d is related to the radial distance R(t) of the target to be measured, and the specific relationship is as shown in the following formula:

[0073]

[0074] Mix the echo signal x R (t) and the transmitted signal x T (t) of the frequency-modulated continuous wave to obtain the required intermediate-frequency signal x IF (t) in the process as shown in the following formula:

[0075]

[0076] Among them, c is the speed of light, λ is the wavelength, and A R is the received signal power when the antenna of the millimeter-wave radar receives the echo. It can be seen that the frequency The visible frequency f b is linearly related to the radial distance R(t) of the target. The phase IF (t) of the intermediate-frequency signal x Due to the distance-related effect of the radar echo at short distances, the phase difference Δφ(t) of this phase noise item can be ignored, so its value is simplified to zero;

[0077] For the intermediate-frequency signal x IF(t) Perform analog-to-digital conversion to obtain the original data of the frequency values of the intermediate-frequency signal within the entire working period (each frame); each frame of the original data is respectively formed into a first matrix including the fast-time dimension and the slow-time dimension, denoted as M[m, n], where m represents the number of sampling points corresponding to a single chirp in the fast-time dimension, and n represents the total number of chirps corresponding to each frame of the original data in the slow-time dimension. It can be seen that the first matrix M[m, n] represents the original data of one frame. Each column contains the frequency values of the intermediate-frequency signal corresponding to m sampling points in a chirp, and there are a total of n columns. The original data of the entire working period contains multiple frames, corresponding to multiple first matrices. In this embodiment, it is preferably set that there are a total of k frames of original data;

[0078] S3. Perform one-dimensional fast Fourier transform on all the first matrices M[m, n] obtained in step S2; specifically including:

[0079] Perform FFT on the first matrix M[m, n] column by column, so as to convert from the inspection frequency to the inspection distance; the matrix obtained by performing the FFT transformation on the first matrix M[m, n] is denoted as the second matrix RP[m, n]. Each row in the second matrix RP[m, n] corresponds to a different distance. It is customary to call each row in the second matrix RP[m, n] a range cell;

[0080] S4. Perform the operation of filtering stationary clutter on all the second matrices RP[m, n] obtained in step S3; specifically including:

[0081] S41. Calculate the mean value of each row in the second matrix RP[m, n] respectively, and then take out the mean values of each row to form a first vector. The dimension of each first vector corresponds to m of each frame of the second matrix RP[m, n];

[0082] S42. Use a window to filter stationary clutter; set the window size to k, and the window acts on k first vectors to form a third matrix of m*k dimensions;

[0083] S43. Subtract the mean value of all the values in each row from each value in each row of the third matrix to obtain the fourth matrix RS[m, k] after filtering stationary clutter;

[0084] S5. Perform one-dimensional constant false alarm rate detection CFAR operation on the fourth matrix RS[m, k] obtained in step S4; specifically including:

[0085] The unit-average constant false alarm rate detector (CA-CFAR) is used to process each column of the fourth matrix RS[m,k] separately. After processing each column, a detection threshold of the same m dimensions is obtained. Then, the values of each dimension of the detection threshold are compared with the values of each dimension of the corresponding column. If the value of the corresponding column is greater than the value of the detection threshold, it is determined that a human target exists in this dimension, and then the row distance cell in the second matrix RP[m,n] corresponding to this dimension is extracted. If the value of the corresponding column is less than the value of the detection threshold, it is determined that no human target exists in this dimension.

[0086] S6. After the distance cells where human targets exist are extracted in step S5, the corresponding phase signals are extracted from the fourth matrix RS[m,k] corresponding to these distance cells, and then the extracted phase signals are phase-unwrapped to obtain phase signals without phase ambiguity problems. Specifically, it includes:

[0087] Extract the two signals of the imaginary part I(t) and the real part Q(t) in the fourth matrix RS[m,k] corresponding to the distance cells, and use the extended-DACM algorithm to perform phase unwrapping on the two signals of the imaginary part I(t) and the real part Q(t) respectively to obtain phase signals without phase ambiguity.

[0088] The process principle of the extended-DACM algorithm is as follows:

[0089] Generally speaking, the phase can be obtained by the arctangent demodulation of the two signals of the imaginary part I(t) and the real part Q(t) for the distance cells. The calculation formula is as follows:

[0090]

[0091] However, the phase obtained by the arctangent demodulation Due to the arctangent function, there will be a problem of phase ambiguity, and the range of the phase is limited to Inside, before unwrapping, the phase will have jump discontinuities. Therefore, when using the extended-DACM algorithm for phase unwrapping, first take the derivative of the after arctangent demodulation to obtain the derivative function ω(t). The calculation formula is as follows:

[0092]

[0093] and are the derivatives of the two signals of the real part and the imaginary part with respect to time respectively. Although It is discontinuous beyond the value range, but the left derivative of the left point and the right derivative of the right point at the discontinuous point exist and are equal. Therefore, by integrating ω(t) again, the discontinuity point can be eliminated to restore the phase signal. For discrete signals, the extended-DACM algorithm uses the forward difference approximation method during differentiation, and the calculation formula is as follows:

[0094]

[0095] where Δt is the chirp period. By cumulative approximate integration of ω[n], the unwrapped phase signal is obtained. The specific calculation formula is as follows:

[0096]

[0097] where n here represents the total number of frames corresponding to the presence of a human target, and k represents the k-th frame in n.

[0098] S7. Detect the vital signs of the human target based on the unwrapped phase signal obtained in step S6, and determine whether there are vital signs in the corresponding range cell; specifically including:

[0099] S71. Select the total number of frames s for judging live detection from the unwrapped phase signal, and form a 1*s-dimensional vector matrix H with the frequency values of the phase signals corresponding to these s frames after unwrapping.

[0100] S72. Perform FFT on the vector matrix H to obtain the fifth matrix HF[1, s], calculate the proportion of the spectral energy in the set frequency range in the total energy of the entire frequency band, and determine whether the proportion of the spectral energy in the set frequency range in the total energy of the entire frequency band exceeds the preset proportion threshold. The calculation formula for the proportion of the spectral energy in the set frequency range in the total energy of the entire frequency band is as follows:

[0101]

[0102] where i and j both represent the frame number respectively, a corresponds to the row number where the human target exists in the fourth matrix RS[m, k] after one-dimensional CFAR in step S5, and n here is the number of points for FFT in the set frequency range. In this embodiment, the preferably set frequency range is 0 - 2 Hz.

[0103] S73. Calculate the variance of the vector matrix H, and determine whether the variance exceeds the preset variance threshold;

[0104] S74. If the proportion of the spectral energy in the set frequency range in the total energy of the entire frequency band and the variance of the vector matrix H both exceed the proportion threshold and the variance threshold, it is determined that the human target has vital signs, and then the range cell of the human target with vital signs is extracted.

[0105] S8. Further extract the chest wall displacement signal from the distance units of the human target with vital signs extracted in step S7; specifically: for the unwrapped phase signal Calculate the chest wall displacement signal R[n], and the calculation formula is as follows:

[0106]

[0107] S9. Use the iterative adjusted time window algorithm to improve the accuracy of the respiratory main frequency f b1 and the signal-to-noise ratio of the chest wall displacement signal to obtain the adjusted chest wall displacement signal; specifically including:

[0108] S91. Take out a set duration of data from the chest wall displacement signal R[n], perform FFT with the time window N, and obtain the chest wall displacement spectrum as Figure 2 shown. Take the frequency of the frequency point where the maximum amplitude of the chest wall displacement spectrum obtained after FFT is located as the respiratory main frequency f b1 , and take the frequency of the point where the maximum amplitude within the frequency range of 1 - 3 Hz of the chest wall displacement spectrum obtained after FFT is located as the heart rate main frequency f h ;

[0109] S92. After obtaining the chest wall displacement spectrum by performing FFT with the time window N in step S91, obtain the respiratory main frequency f b1 , the amplitude of the respiratory main frequency |A1|, and the second harmonic frequency f b2 of respiration from the chest wall displacement spectrum;

[0110] S93. Since performing FFT may cause errors due to spectral leakage, it is necessary to judge whether the respiratory main frequency f b1 is half of the second harmonic frequency f b2 of respiration after completing FFT, and at the same time judge whether the amplitude of the respiratory main frequency is greater than the amplitude of the respiratory main frequency in the previous iteration, that is, |A1| > |A1'|;

[0111] S94. If both and |A1| > |A1'| are not satisfied, then adjust the number of points of the time window N, and return to execute steps S91 to S93 in sequence until both and |A1| > |A1'| are satisfied; if both and |A1| > |A1'| are satisfied;

[0112] For the adjusted chest wall displacement spectrum obtained by performing FFT with the time window N under the condition of simultaneously satisfying and |A1| > |A1'|, its waveform is as Figure 3 shown, compared with Figure 2The chest wall displacement spectrum before adjustment is as follows: After adjustment, the amplitude of the main respiratory frequency is high, the double and triple frequencies of the main respiratory frequency are clearly visible, and the main heartbeat frequency is also clearly visible;

[0113] S10, reconstructing a chest wall displacement-time signal from the adjusted chest wall displacement spectrum obtained in step S9; specifically comprising:

[0114] The chest wall displacement-time signal caused by breathing can be approximated by the following formula:

[0115]

[0116] Among them, ω1, ω2, ω3 are the main frequency, double frequency, and triple frequency of breathing respectively, and A1, A2, and A3 are the amplitudes corresponding to the main frequency, double frequency, and triple frequency of breathing respectively, where the amplitude satisfies

[0117] For the adjusted chest wall displacement spectrum, we know from prior conditions that the peak amplitude of the main respiratory frequency is approximately 3.5 times the peak amplitude of its second frequency, and the peak amplitude of the main respiratory frequency is approximately 10 times the peak amplitude of its third frequency. Based on this prior condition, we find the root of the following formula:

[0118] A3x 3 +A2x 2 +A1x-H(t)=0

[0119] Solve the above equation for the root that meets the prior conditions, and substitute the root to obtain the reconstructed chest wall displacement-time signal. That is, H(t) that meets the prior conditions is the reconstructed chest wall displacement-time signal. Figure 3 and Figure 4 As shown in the figure, the reconstructed chest wall displacement-time signal can effectively suppress frequency doubling and improve the signal-to-noise ratio;

[0120] S11, extracting vital signs of the human target from the chest wall displacement-time signal reconstructed in step S10; specifically comprising:

[0121] The frequency at which the amplitude of the reconstructed chest wall displacement-time signal reaches its maximum is the respiratory frequency f b , the maximum amplitude in the frequency range of 1-3Hz is the heart rate f h ;

[0122] Calculate the number of respirations and heartbeats per minute respectively to obtain the corresponding vital signs of the human target, as shown in the following formula:

[0123] HB=60×f b

[0124] HR=60×f h

[0125] Among them, HB is the number of breaths per minute, and HR is the number of heartbeats per minute.

[0126] Compared with the prior art, the beneficial effects of the vital sign detection method based on millimeter-wave radar in this embodiment are as follows:

[0127] In this embodiment, by performing good denoising, signal screening, and signal reconstruction on the radar echo, and at the same time efficiently characterizing the strength state of the vital signs of the detected human target, false negatives or false positives are avoided, and the detection accuracy is improved; the computational complexity of the related calculation process in this embodiment is small, avoiding the problem that it is difficult to perform rapid response due to the large computational complexity in the process of extracting respiratory and heartbeat signs in the prior art, and increasing the reliability of the measurement.

[0128] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A vital sign detection method based on millimeter-wave radar, characterized in that, The steps are as follows: Use the antenna of the millimeter-wave radar to transmit a frequency-modulated continuous wave (FMCW). Then, the millimeter-wave radar acquires the echo, mixes the echo signal to obtain the original data, and forms each frame of the original data into a first matrix that includes the fast-time dimension and the slow-time dimension. Perform a one-dimensional fast Fourier transform (FFT) on the first matrix to separate the echo signals at different distances, obtaining the corresponding second matrix. Then, perform an operation to filter out stationary clutter on the second matrix. First, use a window to convert the second matrix into a third matrix, and then use the mean value to perform operations on the third matrix to obtain a fourth matrix, thus completing the stationary clutter filtering operation. Perform a one-dimensional constant false alarm rate (CFAR) detection operation on the fourth matrix to determine the range cells where human targets exist. Then, extract the phase signals from the range cells where human targets exist, and use the extended-DACM algorithm to perform an unwrapping operation to obtain the unwrapped phase signals. Judge the vital signs of the human target from the unwrapped phase signals. Extract the corresponding chest wall displacement signals from the range cells of the human targets with vital signs, and then use the iterative adjusted time window algorithm to adjust to obtain the adjusted chest wall displacement signals. Reconstruct the chest wall displacement-time signal from the adjusted chest wall displacement signals, and then obtain the accurate vital signs of the human target from the reconstructed chest wall displacement-time signal.

2. The method for detecting vital signs based on millimeter-wave radar according to claim 1, wherein The specific process of transmitting the frequency-modulated continuous wave (FMCW) is as follows: Using a single-transmitter and single-receiver or single-transmitter and multi-receiver millimeter-wave radar, a frequency-modulated continuous wave (FMCW) is transmitted into the space where vital sign detection is required. The transmitted signal x T (t) waveform is as follows: where t represents the moment, A is the amplitude of the transmitted signal, f s is the starting frequency of the transmitted signal, B is the working bandwidth of the transmitted signal, T c is the duration occupied by a single chirp in the transmitted signal, and φ(t) is the phase noise of the frequency-modulated continuous wave.

3. The method for detecting vital signs based on millimeter-wave radar according to claim 2, wherein The process of mixing the echo signal to obtain the original data is as follows: The acquired echo signal x R (t) is as follows: x R y(t) = αx T (t - t d ) Mix the echo signal x R (t) with the transmitted signal x T (t) of the chirp continuous wave to obtain the desired intermediate frequency signal x IF (t). The process is shown as follows: where c is the speed of light, λ is the wavelength, and A R is the received signal power when the antenna of the millimeter-wave radar receives the echo; Perform analog-to-digital conversion on the intermediate frequency signal x IF (t) to obtain the original data; each frame of the original data is respectively formed into a first matrix including the fast time dimension and the slow time dimension, denoted as M[m,n], where m represents the number of sampling points corresponding to a single chirp in the fast time dimension, and n represents the total number of chirps corresponding to each frame of the original data in the slow time dimension.

4. The method for detecting vital signs based on millimeter-wave radar according to claim 3, wherein The process of performing a one-dimensional fast Fourier transform (FFT) on the first matrix is as follows: Perform a fast Fourier transform on the first matrix M[m,n] column by column. Denote the matrix obtained after performing the fast Fourier transform on the first matrix M[m,n] as the second matrix RP[m,n].

5. The method for detecting vital signs based on a millimeter-wave radar according to claim 4, characterized in that, The process of performing an operation to filter out stationary clutter on the second matrix is as follows: Calculate the mean value of each row in the second matrix RP[m,n], and then take out the mean values of each row to form a first vector. The dimension of each first vector corresponds to m of each frame of the second matrix RP[m,n]. Then, use a window to filter out stationary clutter. Set the window size to k, and the window acts on k first vectors to form a third matrix with dimensions m*k. Subtract the mean value of all the numerical values in each row from each numerical value in each row of the third matrix to obtain the fourth matrix RS[m,k] after filtering out stationary clutter.

6. The method for detecting vital signs based on millimeter-wave radar according to claim 5, characterized in that The process of using the extended-DACM algorithm to perform an unwrapping operation on the range cells where human targets exist is as follows: Extract the two-channel signals of the imaginary part I(t) and the real part Q(t) in the fourth matrix RS[m,k] corresponding to the range cells, and use the extended-DACM algorithm to perform phase unwrapping on the two-channel signals of the imaginary part I(t) and the real part Q(t) respectively to obtain phase signals without phase ambiguity.

7. The method for detecting vital signs based on millimeter-wave radar according to claim 6, wherein The process of detecting the vital signs of the human target from the unwrapped phase signals is as follows: Select the total number of frames s for judging live detection from the unwrapped phase signals, and form a 1*s-dimensional vector matrix H with the frequency values of the phase signals corresponding to these s frames after expansion. Perform a fast Fourier transform on the vector matrix H to obtain the fifth matrix HF[1, s], calculate the proportion of the spectral energy within the set frequency range to the total energy of the entire frequency band, and determine whether the proportion of the spectral energy within the set frequency range to the total energy of the entire frequency band exceeds a preset proportion threshold. The calculation formula for the proportion of the spectral energy within the set frequency range to the total energy of the entire frequency band is as follows: Where i and j respectively represent the frame number, a corresponds to the row number where a human target exists in the fourth matrix RS[m, k] after one-dimensional constant false alarm detection CFAR, and n here is the number of points for fast Fourier transform within the set frequency range; Calculate the variance of the vector matrix H and determine whether the variance exceeds a preset variance threshold; If the proportion of the spectral energy within the set frequency range to the total energy of the entire frequency band and the variance of the vector matrix H both exceed the proportion threshold and the variance threshold, it is determined that the human target has vital signs, and then the range cell of the human target with vital signs is extracted.

8. The method for detecting vital signs based on millimeter-wave radar according to claim 7, wherein The process of adjusting the chest wall displacement signal using the iterative adjusted time window algorithm is as follows: Unwrapped phase signal Calculate the chest wall displacement signal R[n], and the calculation formula is as follows: Extract a segment of data with a set duration from the chest wall displacement signal R[n], perform a fast Fourier transform with a time window N, and use the frequency of the frequency point where the maximum amplitude of the chest wall displacement spectrum obtained after the fast Fourier transform is located as the main respiration frequency f b1 , and use the frequency of the point where the maximum amplitude of the chest wall displacement spectrum obtained after the fast Fourier transform is located within the set frequency as the main heartbeat frequency f h ; Obtain the main respiratory frequency f from the chest wall displacement spectrum b1 , the amplitude of the main respiratory frequency |A1|, and the second harmonic frequency f of respiration b2 ; Determine the main respiration frequency f b1 Whether it is half of the double frequency f b2 of respiration, and at the same time determine whether the amplitude of the main respiration frequency is greater than the amplitude of the main respiration frequency in the previous iteration, that is, determine whether |A1| > |A1′|; if both are not satisfied and |A1| > |A1′|, then after adjusting the number of points of the time window N, perform the fast Fourier transform again until both and |A1| > |A1′| are satisfied.

9. The method for detecting vital signs based on millimeter-wave radar according to claim 8, wherein The process of reconstructing the chest wall displacement-time signal is as follows: The chest wall displacement-time signal caused by breathing is given by the following formula: Where ω1, ω2, and ω3 are respectively the main frequency, second harmonic frequency, and third harmonic frequency of breathing, and A1, A2, and A3 are the amplitudes corresponding to the main frequency, second harmonic frequency, and third harmonic frequency of breathing respectively; For the adjusted chest wall displacement spectrum, find the roots of the following formula according to the prior conditions: A3x 3 +A2x 2 +A1x - H(t) = 0 Solve the roots of the above formula that meet the prior conditions, substitute the roots into the above formula, and obtain the reconstructed chest wall displacement-time signal.

10. The method for detecting vital signs based on millimeter-wave radar according to claim 9, characterized in that, The process of obtaining vital signs from the reconstructed chest wall displacement-time signal is as follows: In the reconstructed chest wall displacement-time signal, the frequency at the point with the maximum amplitude is set as the breathing frequency f b , and the point with the maximum amplitude within the set frequency range is the heart rate f h ; Calculate the number of breaths and heartbeats per minute respectively to obtain the vital signs of the corresponding human target, as shown in the following formula: HB = 60 × f b HR = 60 × f h Where HB is the number of breaths per minute and HR is the number of heartbeats per minute.

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