Vital sign detection method and device of FMCW millimeter wave radar

Abstract

The invention relates to an FMCW millimeter wave radar vital sign detection method and device, and the method comprises the steps: collecting and obtaining radar data, and screening out a target data point in the radar data based on a CFAR algorithm; generating two-dimensional point cloud information of the target data point based on an MUSCI algorithm, and performing density division on the two-dimensional point cloud information by using a DBSCAN clustering algorithm to obtain accurate positioning information; performing unwrapping and first-order difference processing on the accurate positioning information to obtain phase difference data; and parameters of the phase difference data are optimized based on a particle swarm optimization algorithm, and vital sign information is detected. Compared with the prior art, the method has the advantages that the measurement precision is improved, the anti-interference capability is high, the problem of parameter selection of a traditional variational mode decomposition method is well solved, and the interference of high-frequency noise and the like is avoided.

Description

Technical Field

[0001] This invention relates to the field of vital sign detection technology, specifically to a method and device for detecting vital signs using FMCW millimeter-wave radar. Background Technology

[0002] Vital sign detection is a core technological requirement in fields such as healthcare, smart homes, and human-computer interaction. Especially in non-contact scenarios, achieving high-precision, interference-resistant monitoring of vital parameters (such as respiration and heart rate) is of significant research value. While traditional contact sensors (such as ECG electrodes and pulse oximeters) offer high accuracy, they require direct contact with the human body, which can cause discomfort and limits their applicability. Non-contact solutions, such as infrared cameras and WiFi signal reflection, overcome these limitations but face challenges such as ambient light interference, privacy breaches, and insufficient resolution.

[0003] In recent years, frequency-modulated continuous wave (FMCW) millimeter-wave radar technology has become a research hotspot for vital sign detection due to its unique advantages. Millimeter-wave radar transmits high-frequency electromagnetic waves and analyzes reflected signals, enabling non-metallic sensing through clothing and other non-metallic media. Its frequency-modulated continuous wave system combines high range resolution with low transmission power, making it suitable for long-term indoor monitoring. However, existing FMCW radar methods for vital sign detection still face two major challenges: first, complex environments (such as the presence of multiple people or dynamic interference) are susceptible to clutter, making it difficult to extract weak vital signals; second, harmonic coupling effects of vital signs such as respiration and heartbeat, as well as individual differences (such as body shape and posture), can easily lead to algorithm misjudgments, reducing reliability.

[0004] Therefore, there is an urgent need for a new method for detecting vital signs using FMCW millimeter-wave radar to solve the above problems. Summary of the Invention

[0005] This invention provides a vital signs detection method and apparatus for FMCW millimeter-wave radar that overcomes or at least partially solves the above-mentioned problems.

[0006] According to a first aspect of the present invention, the present invention provides a method for detecting vital signs using FMCW millimeter-wave radar, comprising: Collect radar data and filter out target data points in the radar data based on the CFAR algorithm; Two-dimensional point cloud information of the target data points is generated based on the MUSCI algorithm, and the two-dimensional point cloud information is divided into density using the DBSCAN clustering algorithm to obtain precise positioning information. The precise positioning information is unwound and subjected to first-order differential processing to obtain phase difference data; The parameters of the phase difference data are optimized based on the particle swarm optimization algorithm to detect vital signs information.

[0007] Preferably, the acquisition of radar data includes: The collected radar data is arranged in the form of sampling points, pulse count, and a three-dimensional matrix of radar antennas; The distance, velocity, and angle information are obtained by performing FFT transformation on the three-dimensional matrix.

[0008] The target data points in the radar data are selected based on the CFAR algorithm. The CFAR algorithm expression is as follows: , This represents the probability of a false alarm. is the threshold coefficient, and N is the number of reference units.

[0009] Preferably, the step of generating the two-dimensional point cloud information of the target data points based on the MUSCI algorithm includes: defining the covariance matrix R of the received signal as: in, , It is the received signal vector. It is an array steering matrix. It is the source signal vector. It is the received noise vector, and E represents the mathematical expectation. express The conjugate transpose of the covariance matrix R is used to decompose the covariance matrix R into a signal component and a noise component. , It is the covariance matrix of the signal source. It is the covariance matrix of the noise. , It is the identity matrix. This is the variance of the noise; eigenvalue decomposition is performed on the covariance matrix R to obtain the eigenvalues ​​and their corresponding eigenvectors: , It is an eigenvalue. It is the corresponding feature vector; for each assumed signal arrival direction Calculate its array response And calculate the music spectrum: , It is the array's steering vector, indicating the direction from which the array begins to move. Response to arriving signals; It is the eigenvector matrix of the noise space.

[0010] Preferably, the step of using the DBSCAN clustering algorithm to divide the two-dimensional point cloud information into density segments to obtain precise positioning information includes: Set the neighborhood radius ε and the minimum number of points MinPts to identify all core points, boundary points and noise points in the dataset; Starting from any unvisited core point, all density-reachable points are recursively aggregated to form clusters, with unabsorbed low-density points retained as noise to complete the partitioning. Preferably, the step of unwinding and performing first-order differential processing on the precise positioning information to obtain phase difference data includes: ,in, Representing the Channel 1 Phase information of each Chirp; It is the signal after distance FFT processing, representing the signal at the th... Channel, First The first Chirp, the first The complex value at the distance gate, real part and the virtual part They together form orthogonal components; the phase difference between adjacent sampling points is compared one by one, and when the difference exceeds π, 2π is subtracted, and when it is less than -π, 2π is added; The phase signal after phase unwrapping is Extracted using the arctan function It is a phase signal; When the difference value is positive, it indicates that the phase is leading at the previous moment; when the difference value is negative, it indicates that the phase is leading at the next moment; a zero value indicates phase synchronization; and reaching the extreme value of ±π indicates that the two frames of signals are completely out of phase.

[0011] Preferably, the step of optimizing the parameters of the phase difference data based on the particle swarm optimization algorithm to detect vital sign information includes: Based on the particle swarm optimization algorithm, the VMD parameters are adaptively set to decompose and obtain multiple modal components. The first and second modal components correspond to the time frequencies of respiration and heartbeat, respectively; The frequency signals of respiration and heartbeat are obtained by performing Fourier transform on the time and frequency of the respiration and heartbeat. The vital signs information is obtained by processing the frequency signals of breathing and heartbeat.

[0012] A second aspect of the present invention provides a vital signs detection device for FMCW millimeter-wave radar, comprising: The acquisition module is used to acquire radar data and filter out target data points in the radar data based on the CFAR algorithm. The precise positioning module generates two-dimensional point cloud information of the target data points based on the MUSCI algorithm, and uses the DBSCAN clustering algorithm to divide the two-dimensional point cloud information into density to obtain precise positioning information. The phase processing module is used to unwrap and perform first-order differential processing on the precise positioning information to obtain phase difference data; The detection module is used to optimize the parameters of the phase difference data based on the particle swarm optimization algorithm to detect vital signs information.

[0013] A third aspect of the present invention provides a computer device, the computer device including a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements a vital signs detection method for FMCW millimeter-wave radar.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements a vital signs detection method for FMCW millimeter-wave radar.

[0015] The present invention provides a method and apparatus for detecting vital signs in FMCW millimeter-wave radar. This method achieves precise target localization by combining the MUSIC algorithm and the DBSCAN clustering algorithm. The target range-angle gate number obtained from the localization is then extracted, and the target phase is unwrapped and subjected to first-order differential processing. Finally, an improved particle swarm optimization algorithm is used to optimize the variational mode decomposition algorithm parameters to detect the target's vital sign information. This method improves measurement accuracy and has strong anti-interference capabilities, effectively solving the parameter selection problem of traditional variational mode decomposition methods and avoiding interference from high-frequency noise. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a vital signs detection method using an FMCW millimeter-wave radar provided in an embodiment of the present invention; Figure 2 This is a diagram illustrating the single-objective clustering effect provided in an embodiment of the present invention; Figure 3 This is a diagram illustrating the multi-objective clustering effect provided in an embodiment of the present invention; Figure 4 These are comparison images of phase unwinding before and after, provided in an embodiment of the present invention; Figure 5 This is a comparison image of phase differential amplification before and after, provided in an embodiment of the present invention; Figure 6 This is a comparison chart of the fitness curves of the PSO-VMD algorithm before and after improvement provided in this embodiment of the invention; Figure 7 This is a schematic diagram of the structure of a vital signs detection device for an FMCW millimeter-wave radar provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] In response to the problems existing in the background technology, Figure 1 This is a schematic diagram of a vital sign detection method using FMCW millimeter-wave radar provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: 101. Acquire radar data and filter out target data points in the radar data based on the CFAR algorithm; 102. Generate two-dimensional point cloud information of the target data points based on the MUSCI algorithm, and use the DBSCAN clustering algorithm to divide the two-dimensional point cloud information into density to obtain precise positioning information; 103. The precise positioning information is unwound and subjected to first-order differential processing to obtain phase difference data; 104. The parameters of the phase difference data are optimized based on the particle swarm optimization algorithm to detect vital signs information.

[0019] Specifically, in this embodiment of the invention, firstly, in step 101, the data acquired by the FMCW millimeter-wave radar is processed by static clutter filtering, CFAR, etc., then in step 102, the data is precisely located using a combination of the MUSIC algorithm and the DBSCAN clustering algorithm. Next, in step 103, the target range angle gate number is extracted, and the target phase is unwrapped and processed by first-order difference. Finally, in step 104, the improved particle swarm optimization algorithm is used to optimize the variational mode decomposition algorithm parameters to detect the target's vital signs information.

[0020] In summary, the FMCW millimeter-wave radar vital sign detection method provided by this invention improves measurement accuracy and has strong anti-interference capability compared with the prior art. It effectively solves the parameter selection problem of traditional variational mode decomposition methods and avoids interference from high-frequency noise, etc.

[0021] Based on the above embodiments, the acquisition of radar data includes: The collected radar data is arranged in the form of sampling points, pulse count, and a three-dimensional matrix of radar antennas; The distance, velocity, and angle information are obtained by performing FFT transformation on the three-dimensional matrix.

[0022] Specifically, the embodiments of the present invention require a certain degree of preprocessing of the collected radar data. First, the data collected by the radar is arranged in the form of a three-dimensional matrix of sampling points * pulse number * radar antenna.

[0023] Then, by performing Fourier transform (FFT) on the sampling points, pulses, and radar antenna of the radar's three-dimensional matrix, the distance, velocity, and angle information can be obtained.

[0024] Based on the above embodiments, the target data points in the radar data are filtered out using the CFAR algorithm. The CFAR algorithm expression is as follows: , This represents the probability of a false alarm. is the threshold coefficient, and N is the number of reference units.

[0025] In this embodiment of the invention, radar data is preprocessed, and a static clutter filtering algorithm is used to reduce environmental noise. Then, the CFAR algorithm is used to filter out target points. Specifically, the static clutter filtering method is as follows: Among them, using Sampling points representing the distance dimension (fast time). The sampling points represent the velocity dimension (slow time). The formula is: The CFAR algorithm is implemented as follows: False alarm probability It is independent of the actual interference strength and depends only on the threshold coefficient. And the number of reference units N. The analytical expression for the threshold coefficient is: Whether a target exists can be determined by checking whether the unit power is greater than a threshold value.

[0026] Based on the above embodiments, the step of generating the two-dimensional point cloud information of the target data points based on the MUSCI algorithm includes: The covariance matrix R of the received signal is defined as follows: in, , It is the received signal vector. It is an array steering matrix. It is the source signal vector. It is the received noise vector, and E represents the mathematical expectation. express The conjugate transpose of the covariance matrix R is used to decompose the covariance matrix R into a signal component and a noise component. , It is the covariance matrix of the signal source. It is the covariance matrix of the noise. , It is the identity matrix. This is the variance of the noise; eigenvalue decomposition is performed on the covariance matrix R to obtain the eigenvalues ​​and their corresponding eigenvectors: , It is an eigenvalue. It is the corresponding feature vector; for each assumed signal arrival direction Calculate its array response And calculate the music spectrum: , It is the array's steering vector, indicating the direction from which the array begins to move. Response to arriving signals; It is the eigenvector matrix of the noise space.

[0027] Specifically, the MUSIC algorithm is implemented as follows: a received signal is a linear combination of multiple signals and noise. ,in It is the received signal vector. It is an array steering matrix. It is the source signal vector. This is the received noise vector. The covariance matrix R of the received signal is defined as: in Represents the mathematical expectation. express The conjugate transpose of . According to the model, the covariance matrix can be decomposed into a signal component and a noise component: , It is the covariance matrix of the signal source, which is usually assumed to be a diagonal matrix. It is the covariance matrix of the noise, usually assumed to be white noise, i.e. ( It is the identity matrix. (This refers to the variance of the noise). Eigenvalue decomposition is performed on the covariance matrix to obtain the eigenvalues ​​and their corresponding eigenvectors: , It is an eigenvalue. These are the corresponding eigenvectors. The angle of arrival of the signal source is estimated by finding the direction of the signal. The covariance matrix is ​​decomposed into signal space and noise space, with the signal space corresponding to larger eigenvalues ​​and the noise space corresponding to smaller eigenvalues. For each assumed signal direction of arrival... We calculate its array response. And calculate the music spectrum: in It is the array's steering vector, indicating the direction from which the array begins to move. Response to arriving signals; It is the eigenvector matrix of the noise space, containing the eigenvectors of the noise. The minimum value of the MUSIC spectrum corresponds to the direction of arrival (DOA) of the signal source.

[0028] Based on the above embodiments, the step of using the DBSCAN clustering algorithm to perform density division of the two-dimensional point cloud information to obtain precise positioning information includes: Set the neighborhood radius ε and the minimum number of points MinPts to identify all core points, boundary points and noise points in the dataset; Starting from any unvisited core point, recursively aggregate all density-reachable points to form a cluster, and retain low-density points that are not absorbed as noise to complete the partitioning.

[0029] Specifically, the DBSCAN clustering algorithm implementation method provided in this embodiment of the invention is as follows: Define the neighborhood radius ε and the minimum number of points MinPts to identify core points, boundary points and noise points. If the ε neighborhood of a point contains at least MinPts points, a new cluster is formed, and all density-reachable points are recursively aggregated. Finally, high-density areas are divided into clusters, and low-density points are regarded as noise.

[0030] Based on the above embodiments, the step of unwinding and performing first-order differential processing on the precise positioning information to obtain phase difference data includes: in, Representing the Channel 1 Phase information of each Chirp; It is the signal after distance FFT processing, representing the signal at the th... Channel, First The first Chirp, the first The complex value at the distance gate, real part and the virtual part They together form orthogonal components; the phase difference between adjacent sampling points is compared one by one, and when the difference exceeds π, 2π is subtracted, and when it is less than -π, 2π is added; The phase signal after phase unwrapping is Extracted using the arctan function It is a phase signal; When the difference value is positive, it indicates that the phase is leading at the previous moment; when the difference value is negative, it indicates that the phase is leading at the next moment; a zero value indicates phase synchronization; and reaching the extreme value of ±π indicates that the two frames of signals are completely out of phase.

[0031] Specifically, the phase unwinding method is as follows: Representing the Channel 1 Phase information of each Chirp; It is the signal after distance FFT processing, representing the signal at the th... Channel, First The first Chirp, the first A complex value on a distance gate, where the real part and the virtual part Together they form orthogonal components.

[0032] During phase extraction, the use of the arctan function can cause phase wrapping (limited to between -π and π), resulting in a phase jump distortion at π. To restore the true continuous phase change, unwrapping is required: compare the phase difference between adjacent sampling points one by one; when the difference exceeds π, subtract 2π (positive jump); when it is less than -π, add 2π (negative jump). MATLAB provides the unwarp function or a custom algorithm to implement this operation, ultimately obtaining an unwrapped phase curve that accurately reflects thoracic cavity motion.

[0033] The phase signal after phase unwrapping is Extracted using the arctan function It is a phase signal.

[0034] The phase differential amplification method is as follows: The result obtained after phase difference processing The signal has a clear physical meaning: a positive difference value indicates that the phase is leading at the previous moment; a negative difference value indicates that the phase is leading at the next moment; a zero value indicates phase synchronization; and reaching the extreme value of ±π indicates that the two frames of signals are completely out of phase. This differential processing transforms the original phase change information into a more intuitive representation of the phase relationship, with its numerical range strictly controlled within the [-π,π] interval, providing standardized input for subsequent micro-motion feature extraction.

[0035] Based on the above embodiments, the optimization of the parameters of the phase difference data using the particle swarm optimization algorithm to detect vital sign information includes: Based on the particle swarm optimization algorithm, the VMD parameters are adaptively set to decompose and obtain multiple modal components. The first and second modal components correspond to the time frequencies of respiration and heartbeat, respectively; The frequency signals of respiration and heartbeat are obtained by performing Fourier transform on the time and frequency of the respiration and heartbeat. The vital signs information is obtained by processing the frequency signals of breathing and heartbeat.

[0036] The Particle Swarm Optimization (VMD) algorithm addresses the drawback of manually selecting two key parameters—the mode number K and the penalty factor α—using an improved Particle Swarm Optimization approach. The PSO algorithm simulates the foraging behavior of a flock of birds; each "particle" acts like a bird, only needing to record its position and flight speed. The velocity of motion is expressed in vectors This means that the entire group collaborates to find the global optimum by sharing the best positions they each discover.

[0037] particle The position is represented as , In the PSO algorithm, each particle represents a d-dimensional parameter combination. The algorithm evaluates the quality of each particle by calculating its fitness value, and then dynamically updates the particle's velocity and position based on swarm optimization and individual optimization information. This mechanism is similar to a flock of birds constantly adjusting their flight direction through communication, ultimately finding food sources quickly.

[0038] The above formula represents the continuous updating of the particle's velocity and position during global and local searches, where: the number of iterations is n, and the inertia weight is... It is a number in the range 0-1; and Learning factor; randomly adjusted parameter Set between 0 and 1. and Representing particles The velocity and position at the nth iteration; Representative particles The position of the individual extreme value at the nth iteration; This represents the position of the global extremum in the nth iteration. Inertia weight. The value determines the algorithm's global search capability; a larger value indicates better suitability for global search, while a smaller value indicates better suitability for local search. However, in traditional PSO-VMD... The algorithm uses a fixed value, which leads to fast iteration speeds during the initial global search but significantly slower speeds and poor convergence during later local searches. Therefore, this paper improves the PSO algorithm by setting an inertia weight. Using a monotonically decreasing concave function accelerates convergence, allowing the algorithm to maintain its early-stage search capabilities while also achieving faster speeds in later local searches, thus improving search efficiency. The formula is as follows: Where n is the number of iterations and N is the maximum number of iterations. Controlling the deceleration rate of the inertia weight, a smaller This allows the weights to decrease more slowly, maintaining a certain global search capability for the particles; larger This will cause particles to quickly reduce their inertial weight and focus more on local search.

[0039] The learning factor also affects search performance; similar to inertia weights, it should be set relatively large initially. , Value, enhances global search capabilities, requires less later , To enhance local search capabilities, this paper changes the previously fixed learning factor to a dynamically adjusted function. The learning factor update formula is as follows: In the formula: , These are the upper and lower limits of the learning factor.

[0040] When using the PSO algorithm for optimization, the effectiveness of each particle's current iteration needs to be judged by the fitness function. The fitness function is a function used to measure the merits of each particle's position in the search space. The goal of PSO is to continuously optimize the fitness function by updating the particle's position and velocity to find the global optimum. Generally, the fitness function is set according to the specific problem. Since this paper measures signals related to time series, sample entropy is used as the fitness function. Sample entropy is a statistic used to measure the complexity of time series, especially the degree of irregularity and randomness in a signal. It is an improved version of approximate entropy. By considering the local dependencies of the data sequence, sample entropy can provide a more accurate measure of signal complexity and has better robustness.

[0041] To verify the technical effects of the embodiments of the present invention, simulation verification was performed. Figure 2 This is a diagram illustrating the single-objective clustering effect provided in an embodiment of the present invention, such as... Figure 2 The image shown is a target azimuth map calculated using the DBSCAN clustering algorithm at a single target distance of 1m from the radar. Figure 3 This is a diagram illustrating the multi-objective clustering effect provided in an embodiment of the present invention, such as... Figure 3 As shown, the target orientation maps are calculated by the DBSCAN clustering algorithm, showing the three targets located at 1.2m 14°, 1.5m 30°, and 1.8m 45° respectively. Figure 4 These are comparison images of phase unwinding before and after, provided in an embodiment of the present invention, as shown below. Figure 4 As shown, after phase unwinding, the phase diagram clearly shows the changes in the pleural undulations of the target.

[0042] Figure 5 These are comparison images of phase differential amplification before and after, provided in an embodiment of the present invention. Figure 6 This is a comparison chart of the fitness curves before and after the improvement of the PSO-VMD algorithm provided in this embodiment of the invention, combined with... Figure 5 and Figure 6As shown in the figure, using sample entropy as the fitness function, the improved PSO-VMD converges faster and has a smaller fitness value than the traditional PSO-VMD. This indicates that the improved algorithm can iterate to find the optimal parameters faster and more accurately, and has better robustness.

[0043] The experimental data table is as follows: Table 1 Comparison of respiratory and heart rates obtained from PSO-VMD optimization

[0044] This table shows the single-target respiratory heart rate measured at different distances using the method described in the embodiments of the present invention. The average result of the rate.

[0045] Table 2. Algorithm-estimated respiratory error values

[0046] This table shows the error of single-target respiration measured at a distance of about 1m by using the method of the embodiments of the present invention and comparing it with other traditional algorithms.

[0047] Table 3. Error values ​​of the algorithm for estimating heart rate

[0048] This table shows the error of single-target heart rate measured at a distance of about 1m by using the method of the embodiments of the present invention and comparing it with other traditional algorithms.

[0049] Table 4. Calculation results of multiple people's breathing

[0050] This table shows the results of multi-target respiration measurements at different distances and angles using the method described in the embodiments of the present invention.

[0051] Table 5. Results of heart rate calculation for multiple people

[0052] This table shows the results of multi-target heart rate measurements under different distances and angles using the method of this invention. The respiratory difference obtained using the method of this invention can be maintained within 4 bpm, and the heart rate can be maintained within 7 bpm. It can be seen that the multi-person respiratory and heart rate detection method proposed in this invention has the advantages of high accuracy, high reliability, and good stability.

[0053] Figure 7 This is a schematic diagram of a vital signs detection device for an FMCW millimeter-wave radar provided in an embodiment of the present invention, as shown below. Figure 7As shown, it includes: an acquisition module 701, a precise positioning module 702, a phase processing module 703, and a detection module 704, wherein: The acquisition module 701 is used to acquire radar data and filter out target data points in the radar data based on the CFAR algorithm. The precise positioning module 702 is used to generate two-dimensional point cloud information of the target data points based on the MUSCI algorithm, and to divide the two-dimensional point cloud information into density using the DBSCAN clustering algorithm to obtain precise positioning information. Phase processing module 703 is used to unwrap and perform first-order differential processing on the precise positioning information to obtain phase difference data; The detection module 704 is used to optimize the parameters of the phase difference data based on the particle swarm optimization algorithm to detect vital signs information.

[0054] The FMCW millimeter-wave radar vital sign detection device provided by the present invention is used to execute the FMCW millimeter-wave radar vital sign detection method provided in the above embodiments. The vital sign detection method of FMCW millimeter-wave radar has been described in detail in the above embodiments, and the device will not be described again in this embodiment.

[0055] This embodiment provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the methods provided in the above-described method embodiments. These instructions include, for example: acquiring radar data and filtering target data points from the radar data based on the CFAR algorithm; generating two-dimensional point cloud information of the target data points based on the MUSCI algorithm, and using the DBSCAN clustering algorithm to perform density division on the two-dimensional point cloud information to obtain precise positioning information; performing unwinding and first-order difference processing on the precise positioning information to obtain phase difference data; and optimizing the parameters of the phase difference data based on the particle swarm optimization algorithm to detect vital signs information.

[0056] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting vital signs using FMCW millimeter-wave radar, characterized in that, include: Collect radar data and filter out target data points in the radar data based on the CFAR algorithm; Two-dimensional point cloud information of the target data points is generated based on the MUSCI algorithm, and the two-dimensional point cloud information is divided into density using the DBSCAN clustering algorithm to obtain precise positioning information. The precise positioning information is unwound and subjected to first-order differential processing to obtain phase difference data; The parameters of the phase difference data are optimized based on the particle swarm optimization algorithm to detect vital signs information.

2. The vital sign detection method of FMCW millimeter-wave radar according to claim 1, characterized in that, The acquisition of radar data includes: The collected radar data is arranged in the form of sampling points, pulse count, and a three-dimensional matrix of radar antennas; The distance, velocity, and angle information are obtained by performing FFT transformation on the three-dimensional matrix.

3. The vital sign detection method of FMCW millimeter-wave radar according to claim 1, characterized in that, The target data points in the radar data are selected based on the CFAR algorithm. The CFAR algorithm expression is: , This represents the probability of a false alarm. is the threshold coefficient, and N is the number of reference units.

4. The vital signs detection method of FMCW millimeter-wave radar according to claim 1, characterized in that, The generation of the two-dimensional point cloud information of the target data points based on the MUSCI algorithm includes: The covariance matrix R of the received signal is defined as follows: in, , It is the received signal vector. It is an array steering matrix. It is the source signal vector. It is the received noise vector, and E represents the mathematical expectation. express The conjugate transpose of the covariance matrix R is used to decompose the covariance matrix R into a signal component and a noise component. , It is the covariance matrix of the signal source. It is the covariance matrix of the noise. , It is the identity matrix. This is the variance of the noise; eigenvalue decomposition is performed on the covariance matrix R to obtain the eigenvalues ​​and their corresponding eigenvectors: , It is an eigenvalue. It is the corresponding feature vector; for each assumed signal arrival direction Calculate its array response And calculate the music spectrum: , It is the array's steering vector, indicating the direction from which the array begins to move. Response to arriving signals; It is the eigenvector matrix of the noise space.

5. The vital signs detection method of FMCW millimeter-wave radar according to claim 1, characterized in that, The process of using the DBSCAN clustering algorithm to divide the two-dimensional point cloud information into density segments to obtain precise positioning information includes: Set the neighborhood radius ε and the minimum number of points MinPts to identify all core points, boundary points and noise points in the dataset; Starting from any unvisited core point, recursively aggregate all density-reachable points to form a cluster, and retain low-density points that are not absorbed as noise to complete the partitioning.

6. The vital signs detection method of FMCW millimeter-wave radar according to claim 1, characterized in that, The step of unwinding and performing first-order differential processing on the precise positioning information to obtain phase difference data includes: ,in, Representing the Channel 1 Phase information of each Chirp; It is the signal after distance FFT processing, representing the signal at the th... Channel, First The first Chirp, the first The complex value at the distance gate, real part and the virtual part Together they form orthogonal components; Compare the phase difference between adjacent sampling points one by one. When the difference exceeds π, subtract 2π. When it is less than -π, add 2π. The phase signal after phase unwrapping is Extracted using the arctan function It is a phase signal; When the difference value is positive, it indicates that the phase is leading at the previous moment; when the difference value is negative, it indicates that the phase is leading at the next moment; a zero value indicates phase synchronization; and reaching the extreme value of ±π indicates that the two frames of signals are completely out of phase.

7. The vital signs detection method of FMCW millimeter-wave radar according to claim 1, characterized in that, The optimization of the phase difference data based on the particle swarm optimization algorithm to detect vital sign information includes: Based on the particle swarm optimization algorithm, the VMD parameters are adaptively set to decompose and obtain multiple modal components. The first and second modal components correspond to the time frequencies of respiration and heartbeat, respectively; The frequency signals of respiration and heartbeat are obtained by performing Fourier transform on the time and frequency of the respiration and heartbeat. The vital signs information is obtained by processing the frequency signals of breathing and heartbeat.

8. A vital signs detection device for FMCW millimeter-wave radar, characterized in that, include: The acquisition module is used to acquire radar data and filter out target data points in the radar data based on the CFAR algorithm. The precise positioning module generates two-dimensional point cloud information of the target data points based on the MUSCI algorithm, and uses the DBSCAN clustering algorithm to divide the two-dimensional point cloud information into density to obtain precise positioning information. The phase processing module is used to unwrap and perform first-order differential processing on the precise positioning information to obtain phase difference data; The detection module is used to optimize the parameters of the phase difference data based on the particle swarm optimization algorithm to detect vital signs information.

9. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein the computer program, when executed by the processor, performs the steps as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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