A method and system for detecting main characteristic breathing and heart rate of millimeter wave radar

Abstract

The present invention proposes a method and system for detecting main characteristic respiration and heart rate of millimeter wave radar. The present invention obtains the chest intermediate frequency signal collected by the millimeter wave radar hardware board and preprocesses it to obtain a first signal; the first signal is processed based on the mean principal component analysis algorithm to obtain a respiration signal and a heartbeat signal; the respiration signal is processed based on the FFT spectrum estimation algorithm to obtain the respiration rate, and the heartbeat signal is processed based on the FFT spectrum estimation algorithm to obtain the heart rate. The mean principal component analysis algorithm adopted by the present invention can accurately separate the respiration signal and the heartbeat signal from the chest signal according to the characteristic value, and can improve the accuracy and real-time performance of the linear frequency modulation millimeter wave radar for human respiration and heart rate detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of millimeter wave radar application, and in particular relates to a method and system for detecting main characteristic breathing and heart rate of millimeter wave radar. Background Art

[0002] Breathing and heartbeat are important vital signs of the human body and can reflect the health of the human body. Respiration rate refers to the number of breathing cycles of a person within 1 minute, and heart rate corresponds to the number of heartbeats per minute. The standard breathing rate and heart rate of a healthy adult are 12 to 20 times and 60 to 100 times per minute, respectively. Currently, the commonly used methods for detecting breathing and heartbeat signals are electrocardiographs (ECG) and photoplethysmography (PPG), but both of these detection methods are contact detection, which has great limitations when detecting related signals of patients with severe infections or severe burns. In order to meet new needs, the use of non-contact vital sign detection technology to achieve research on breathing rate and heart rate has received widespread attention. Non-contact vital sign detection technology mainly detects vital signs through infrared, electromagnetic waves, videos, etc. With the development of semiconductor technology, millimeter-wave radar chips are becoming more and more mature, so the detection of vital sign signals through millimeter-wave radar has gradually become a research hotspot in non-contact detection methods. Millimeter-wave radar emits detection signals in a specific frequency band. When the radar echo signal reflected from the human chest surface is collected, it is processed by a mixer and an analog filter to obtain micro-motion information including breathing and heartbeat. At present, millimeter-wave radar detection of breathing and heartbeat signals has more application scenarios than contact detection methods, such as detecting the breathing and heartbeat signals of drunk people and detecting the vital signs of patients with severe burns.

[0003] However, in the process of using millimeter-wave radar detection, interference signals such as environmental clutter, noise, and white noise generated by hardware heating seriously affect the separation of human breathing signals and heartbeat signals, which in turn affects the estimation of breathing rate and heart rate. To address this problem, traditional methods use DACM algorithm, phase difference, etc. to improve this problem, but problems still exist. In addition, traditional methods use a combination of DACM algorithm and EEMD algorithm to improve the problem of noise interference and poor separation of breathing and heartbeat signals. However, in the research process, the versatility of the algorithm and the accuracy of detection still need to be further improved. Summary of the invention

[0004] Aiming at the problem that interference signals such as environmental clutter, noise, and white noise generated by hardware heating in the process of millimeter-wave radar respiration and heart rate detection seriously affect the separation of human respiration signal and heartbeat signal, the present invention proposes a main feature respiration and heart rate detection method and system of millimeter-wave radar, which realizes real-time and high-precision detection of human respiration rate and heart rate by adopting MPCA algorithm to separate respiration signal and heartbeat signal.

[0005] The technical solution of the method of the present invention is a method for detecting the main characteristic breathing and heart rate of a millimeter wave radar, comprising:

[0006] Step 1: Obtain the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal;

[0007] Step 2: Process the first signal based on the mean principal component analysis algorithm to obtain a breathing signal and a heartbeat signal;

[0008] Step 3: Process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate.

[0009] Preferably, the specific steps of the pretreatment in step 1 include:

[0010] Perform fast Fourier transform on the intermediate frequency signal collected by the millimeter wave radar;

[0011] The intermediate frequency signal after Fourier transformation is reorganized and the phase signal of the chest position is obtained by using the inverse tangent algorithm;

[0012] The first signal is obtained by performing phase differentiation on the obtained phase signal.

[0013] Preferably, in step 2, based on the mean principal component analysis algorithm, the autocorrelation matrix and the covariance matrix of the first signal are calculated, and the eigenvalues ​​of the autocorrelation matrix are obtained, and the average eigenvalues ​​of all the obtained autocorrelation matrix eigenvalues ​​are averaged to obtain the average eigenvalue;

[0014] The eigenvalue range of the respiratory and heartbeat signals is obtained according to the average eigenvalue, and the eigenvector and eigenvector matrix of the respiratory signal and the eigenvector and eigenvector matrix of the heartbeat signal are determined respectively according to the amplitude characteristic that the amplitude of the respiratory signal is more than ten times that of the heartbeat signal, and then the coefficient vector of the respiratory and heartbeat signals is obtained by solving;

[0015] The respiratory signal and the heartbeat signal are estimated according to the respiratory and heartbeat signal coefficient vector obtained by solving the solution.

[0016] Preferably, in step 2, the mean principal component analysis algorithm is based on the process of obtaining the chest intermediate frequency signal collected by the millimeter wave radar hardware board, and the collected chest intermediate frequency signal has a zero mean characteristic, x(n) is the chest intermediate frequency signal at time n, where n∈[1,N], and the expected E[X] of the chest intermediate frequency signal at N times is:

[0017] E[X]=0

[0018] Define the vector, X = x(1), x(2)…x(N), from which we can get the autocorrelation matrix R of Xx and the covariance matrix C x It has the following representation:

[0019] R x =E[X·X H ]

[0020] C x =E{[X-μ X ][X-μ X ] H}

[0021] Among them, X H is the conjugate transposed matrix of X, μ X is the mean of vector X, and E is the mathematical expectation.

[0022] As a preference, the autocorrelation matrix R of the signal X is based on the mean principal component analysis algorithm in step 2. x and the covariance matrix C x should be equal; thus the autocorrelation matrix R can be solved x The K eigenvalues ​​of K , where k∈[1,K], the average of the K eigenvalues ​​obtained is obtained

[0023] The kth eigenvalue λ k The solution formula is as follows:

[0024] R x b=λ k b, where b is a nonzero vector.

[0025] As a preference, the mean principal component analysis algorithm described in step 2 is used to adaptively solve each eigenvalue.

[0026] λ k , k∈[1,K], it is necessary to judge whether it satisfies certain constraints; retain the eigenvalues ​​that meet the constraints and discard the eigenvalues ​​that do not meet the constraints; the adaptive constraints are as follows:

[0027] Constraint 1:

[0028] Constraint 2: λ i t+1 =τ2×λ i t +τ1×(λ j t +τ3×λ i t -λ j t )

[0029] Where τ1 is a random number based on normal distribution, τ2 is a random value in [0,1], and λ i is the autocorrelation matrix R x The i-th eigenvalue of j is the autocorrelation matrix R x The jth eigenvalue of is the autocorrelation matrix R x The eigenvalue of the tth iteration, is the autocorrelation matrix R x The eigenvalue of the t+1th iteration, τ3 is a random value generated between 0 and 1;

[0030] Select the eigenvalue λ that satisfies constraints 1 and 2 i , according to the adaptive solution, the corresponding eigenvalue λ that can be used for subsequent threshold selection can be obtained; the adaptive model formula is as follows:

[0031]

[0032] Among them, % is the remainder symbol.

[0033] Preferably, in step 2, based on the mean principal component analysis algorithm, for the eigenvalues ​​obtained by adaptive solution, the respiratory signal amplitude is more than 10 times the heartbeat signal amplitude, and the respiratory signal eigenvector q corresponding to each eigenvalue is obtained respectively. g and the heartbeat signal feature vector q m , the expression formula of eigenvalue to eigenvector is as follows:

[0034] λ·q m =R x ·q m

[0035] λ·q g =Rx·q g

[0036] And the eigenvector q g Arrange them in columns to form the eigenvector matrix Q g ; The feature vector q m Arrange them in columns to form the eigenvector matrix Q m ; So the coefficient vector y of the breathing signal and heartbeat signal at the nth moment is obtained g (n) and y m (n),

[0037] y g =(y g (1) y g (2), ..., y g (N)

[0038] y m =(y m (1) y m (2), ..., y m (N)), where n∈[1,N]

[0039] According to the coefficient vector y of the breathing signal and heartbeat signal at the nth moment g (n) and y m (n), the breathing signal at this time can be estimated and heartbeat signal The specific formula is as follows:

[0040]

[0041] The present invention also provides a main characteristic breathing and heart rate detection system of millimeter wave radar, which is characterized by: comprising

[0042] A first signal acquisition module is used to acquire the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal;

[0043] A first signal processing module, used for processing the first signal based on a mean principal component analysis algorithm to obtain a respiratory signal and a heartbeat signal;

[0044] The heart rate acquisition module is used to process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and to process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate.

[0045] The beneficial effects of the present invention are as follows:

[0046] The present invention discloses a method and system for detecting main characteristic breathing and heart rate of a millimeter wave radar, which adopts an MPCA algorithm to improve the influence of interference signals on the separation of breathing signals and heartbeat signals.

[0047] The present invention discloses a main characteristic breathing and heart rate detection method and system of a millimeter wave radar, which adopts a mean principal component analysis algorithm capable of accurately separating a breathing signal and a heartbeat signal from a chest cavity signal according to characteristic values.

[0048] The present invention discloses a method and system for detecting the main characteristic breathing and heart rate of a millimeter wave radar, which can improve the accuracy and real-time performance of linear frequency modulation millimeter wave radar in detecting the breathing and heart rate of a human body. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 : A flow chart of a method according to an embodiment of the present invention.

[0050] Figure 2 : The present invention of the embodiment of the present invention is a respiratory signal spectrum diagram.

[0051] Figure 3 : The present invention of the embodiment of the present invention is a heartbeat signal spectrum diagram.

[0052] Figure 4 : The present invention of the embodiment of the present invention is a heart rate detection result diagram.

[0053] Figure 5 : The present invention of the embodiment of the present invention is a respiratory rate detection result diagram. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] In specific implementation, the method proposed in the technical solution of the present invention can be implemented by technical personnel in this field using computer software technology to realize automatic operation process. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of the present invention and computer equipment running the corresponding computer program, should also be within the scope of protection of the present invention.

[0056] The following is combined with Figure 1-5 The specific implementation of the present invention is a method and system for detecting the main characteristic breathing and heart rate of a millimeter wave radar, which is as follows:

[0057] like Figure 1 As shown, the specific implementation of the method of the present invention is a method for accurately detecting respiratory heart rate using a millimeter wave radar, and the specific implementation steps are as follows:

[0058] Step 1: Obtain the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal;

[0059] The specific steps of the pretreatment in step 1 include:

[0060] Perform fast Fourier transform on the intermediate frequency signal collected by the millimeter wave radar;

[0061] The intermediate frequency signal after Fourier transformation is reorganized and the phase signal of the chest position is obtained by using the inverse tangent algorithm;

[0062] The first signal is obtained by performing phase differentiation on the obtained phase signal.

[0063] Step 2: Process the first signal based on the mean principal component analysis algorithm to obtain a breathing signal and a heartbeat signal;

[0064] In step 2, based on the mean principal component analysis algorithm, the autocorrelation matrix and covariance matrix of the first signal are calculated, and the eigenvalues ​​of the autocorrelation matrix are obtained, and the average eigenvalues ​​of all the obtained autocorrelation matrix eigenvalues ​​are averaged to obtain the average eigenvalue;

[0065] The eigenvalue range of the respiratory and heartbeat signals is obtained according to the average eigenvalue, and the eigenvector and eigenvector matrix of the respiratory signal and the eigenvector and eigenvector matrix of the heartbeat signal are determined respectively according to the amplitude characteristic that the amplitude of the respiratory signal is more than ten times that of the heartbeat signal, and then the coefficient vector of the respiratory and heartbeat signals is obtained by solving;

[0066] The respiratory signal and the heartbeat signal are estimated according to the respiratory and heartbeat signal coefficient vector obtained by solving the solution.

[0067] In step 2, the mean principal component analysis algorithm is based on the process of obtaining the chest intermediate frequency signal collected by the millimeter wave radar hardware board. The collected chest intermediate frequency signal has a zero mean characteristic. x(n) is the chest intermediate frequency signal at time n, where n∈[1,N], so the expected chest intermediate frequency signal E[X] is:

[0068] E[X]=0

[0069] Define the vector, X = x(1), x(2)…x(N), from which we can get the autocorrelation matrix R of X x and the covariance matrix C x It has the following representation:

[0070] R x =E[X·X H ]

[0071] C x =E{[X-μ X ][X-μ X ] H}

[0072] Among them, X H is the conjugate transposed matrix of X, μ X is the mean of vector X, and E is the mathematical expectation.

[0073] Based on the mean principal component analysis algorithm described in step 2, the autocorrelation matrix R of signal X x and the covariance matrix C x should be equal; thus the autocorrelation matrix R can be solved x The K eigenvalues ​​of K , where k∈[1,K], the average of the K eigenvalues ​​obtained is obtained

[0074] The kth eigenvalue λ k The solution formula is as follows:

[0075] R x b=λ k b, where b is a nonzero vector.

[0076] Step 2 describes the mean principal component analysis algorithm, for each eigenvalue λ obtained by adaptive solution k , k∈[1,K], it is necessary to judge whether it satisfies certain constraints; retain the eigenvalues ​​that meet the constraints and discard the eigenvalues ​​that do not meet the constraints; the adaptive constraints are as follows:

[0077] Constraint 1:

[0078] Constraint 2: λ i t+1 =τ2×λ i t +τ1×(λ j t +τ3×λ i t -λ j t )

[0079] Where τ1 is a random number based on normal distribution, τ2 is a random value in [0,1], and λ i is the autocorrelation matrix R x The i-th eigenvalue of j is the autocorrelation matrix R x The jth eigenvalue of is the autocorrelation matrix R x The eigenvalue of the tth iteration, is the autocorrelation matrix R x The eigenvalue of the t+1th iteration, τ3 is a random value generated between 0 and 1;

[0080] Select the eigenvalue λ that satisfies constraints 1 and 2 i , according to the adaptive solution, the corresponding eigenvalue λ that can be used for subsequent threshold selection can be obtained; the adaptive model formula is as follows:

[0081]

[0082] Among them, % is the remainder symbol.

[0083] In step 2, based on the mean principal component analysis algorithm, for the eigenvalues ​​obtained by adaptive solution, the respiratory signal amplitude is more than 10 times the heartbeat signal amplitude, and the respiratory signal eigenvector q corresponding to each eigenvalue is obtained respectively. gand the heartbeat signal feature vector q m , the expression formula of eigenvalue to eigenvector is as follows:

[0084] λ·q m =R x ·q m

[0085] λ·q g =Rx·q g

[0086] And the eigenvector q g Arrange them in columns to form the eigenvector matrix Q g ; The feature vector q m Arrange them in columns to form the eigenvector matrix Q m ; So the coefficient vector y of the breathing signal and heartbeat signal at the nth moment is obtained g (n) and y m (n),

[0087] y g =(y g (1) y g (2), ..., y g (N)

[0088] y m =(y m (1) y m (2), ..., y m (N)), where n∈[1,N]

[0089] According to the coefficient vector y of the breathing signal and heartbeat signal at the nth moment g (n) and y m (n), the breathing signal at this time can be estimated and heartbeat signal The specific formula is as follows:

[0090]

[0091] Step 3: Process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate.

[0092] The present invention also provides a main characteristic breathing and heart rate detection system of millimeter wave radar, which is characterized by: comprising

[0093] A first signal acquisition module is used to acquire the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal;

[0094] A first signal processing module, used for processing the first signal based on a mean principal component analysis algorithm to obtain a respiratory signal and a heartbeat signal;

[0095] The heart rate acquisition module is used to process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and to process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate.

[0096] The specific implementation of the system of the present invention is a main characteristic breathing and heart rate detection system of a millimeter wave radar, which is used to implement the main characteristic breathing and heart rate detection method of a millimeter wave radar described in steps 1 to 3.

[0097] The main characteristic breathing and heart rate detection system of the millimeter wave radar includes

[0098] A first signal acquisition module is used to acquire the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal;

[0099] A first signal processing module, used for processing the first signal based on a mean principal component analysis algorithm to obtain a respiratory signal and a heartbeat signal;

[0100] The heart rate acquisition module is used to process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and to process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate.

[0101] The heart rate acquisition module is used for the breathing and heartbeat signals after the breathing and heartbeat signal separation module, and estimates the breathing rate and heart rate using the FFT spectrum estimation method. The estimation method is as follows:

[0102] BR=f BR ×60(times / min)

[0103] HR = f HR ×60(times / min)

[0104] First from Figure 2 and Figure 3 Find the peak value in the FFT spectrum of the respiratory signal as the respiratory frequency f BR , the peak value in the FFT spectrum of the heartbeat signal is taken as the heartbeat frequency f HR . Then use the above formula to convert the obtained respiratory frequency and heart rate into respiratory rate BR and heart rate HR.

[0105] In order to evaluate the accuracy of the main feature breathing and heart rate detection system of the millimeter-wave radar proposed in the present invention, 50 related experiments were conducted. In the experiment, the height of the millimeter-wave radar kit was set to 1 meter, the radial distance between the millimeter-wave radar board and the subject was set to 1 meter, and the debris around the tester was cleared. The tester sat in front of the millimeter-wave radar board, kept the body still, and wore a 712T patient monitor. The heart rate data and respiratory rate data collected by the 712T patient monitor were used as reference values. The millimeter-wave radar board is used to detect the human body's respiratory rate and heart rate in real time, and the collected raw data is subjected to algorithm analysis. The effect of the experiment is verified through 50 groups of experimental samples. By Figure 4 and Figure 5 The results showed that the average heart rate was 79.96 times / min, with a variance of 1.12; the average respiratory rate was 16.81 times per minute, with a variance of 0.78.

[0106] Figure 4 The reference value is the detection value of a commercial instrument, the bandpass filtering method is the detection value processed by the bandpass filter in the original algorithm in the millimeter wave radar hardware board, and the method in this article is the detection value processed by the method described in the content of the present invention.

[0107] Figure 5 The reference value is the detection value of a commercial instrument, the bandpass filtering method is the detection value processed by the bandpass filter in the original algorithm in the millimeter wave radar hardware board, and the method in this article is the detection value processed by the method described in the content of the present invention.

[0108] In the 50 groups of experimental samples, the detection error ranges of respiratory rate and heart rate were 0-17.29% and 0-3.22%, respectively. In the calculation of detection accuracy, the maximum error value is set as the detection error, which is used as the error true value for calculating accuracy. It can be obtained that the accuracy of respiratory rate detection reaches 82.71%, while the accuracy of heart rate detection reaches 96.78%. The measured data show that the main feature respiratory heart rate detection system of the millimeter wave radar disclosed in the present invention has high accuracy.

[0109] In summary, after the present invention obtains the intermediate frequency signal through the millimeter wave radar, the inverse tangent algorithm is used to obtain the phase signal of the chest position; then the MPCA algorithm is used to obtain the respiratory signal and the heartbeat signal; finally, the heart rate and respiratory rate are obtained through FFT spectrum estimation. The present invention uses millimeter wave radar to detect the human body's respiratory heart rate in real time and can detect the heart rate and respiratory rate more accurately, which has practical value for the detection of human vital signs and health monitoring using millimeter wave radar.

[0110] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0115] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of patent protection of the present invention. Under the enlightenment of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested for the present invention shall be based on the attached claims.

Claims

1. A method for detecting breathing and heart rate using a millimeter wave radar as the main feature, characterized in that: Step 1: Obtain the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal; Step 2: Process the first signal based on the mean principal component analysis algorithm to obtain a breathing signal and a heartbeat signal; Step 3: Process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate; The specific steps of the pretreatment in step 1 include: Perform fast Fourier transform on the intermediate frequency signal collected by the millimeter wave radar; The intermediate frequency signal after Fourier transformation is reorganized and the phase signal of the chest position is obtained by using the inverse tangent algorithm; Performing phase differentiation on the obtained phase signal to obtain a first signal; In step 2, based on the mean principal component analysis algorithm, the autocorrelation matrix and covariance matrix of the first signal are calculated, and the eigenvalues ​​of the autocorrelation matrix are obtained, and the average eigenvalues ​​of all the obtained autocorrelation matrix eigenvalues ​​are averaged to obtain the average eigenvalue; The eigenvalue range of the respiratory and heartbeat signals is obtained according to the average eigenvalue, and the eigenvector and eigenvector matrix of the respiratory signal and the eigenvector and eigenvector matrix of the heartbeat signal are determined respectively according to the amplitude characteristic that the amplitude of the respiratory signal is more than ten times that of the heartbeat signal, and then the coefficient vector of the respiratory and heartbeat signals is obtained by solving; The respiratory signal and the heartbeat signal are estimated according to the respiratory and heartbeat signal coefficient vector obtained by solving the solution.

2. The main characteristic breathing and heart rate detection method of millimeter wave radar according to claim 1 is characterized in that: In step 2, the mean principal component analysis algorithm is based on the process of obtaining the chest intermediate frequency signal collected by the millimeter wave radar hardware board. The collected chest intermediate frequency signal has a zero mean characteristic. x(n) is the chest intermediate frequency signal at time n, where n∈[1,N], and the expected E[X] of the chest intermediate frequency signal at N times is: E[X]=0 Define the vector, X = x(1), x(2)…x(N), from which we can get the autocorrelation matrix R of X x and the covariance matrix C x It has the following representation: R x =E(X·X H ] C x =E{[X-μ X ][X-μ X ] H } Among them, X H is the conjugate transposed matrix of X, μ X is the mean of vector X, and E is the mathematical expectation.

3. The main characteristic breathing and heart rate detection method of millimeter wave radar according to claim 2 is characterized in that: Based on the mean principal component analysis algorithm described in step 2, the autocorrelation matrix R of signal X x and the covariance matrix C x should be equal; thus the autocorrelation matrix R can be solved x The K eigenvalues ​​of K , where k∈[1,K], the average of the K eigenvalues ​​obtained is obtained The kth eigenvalue λ k The solution formula is as follows: R x b=λ k b, where b is a nonzero vector.

4. The main characteristic breathing and heart rate detection method of millimeter wave radar according to claim 3 is characterized in that: Step 2 describes the mean principal component analysis algorithm, for each eigenvalue λ obtained by adaptive solution k , k∈[1,K], it is necessary to judge whether it satisfies certain constraints; retain the eigenvalues ​​that meet the constraints and discard the eigenvalues ​​that do not meet the constraints; the adaptive constraints are as follows: Constraint 1: Constraint 2: λ i t+1 =τ2×λ i t +τ1×(λ j t +τ3×λ i t -λ j t ) Where τ1 is a random number based on normal distribution, τ2 is a random value in [0,1], and λ i is the autocorrelation matrix R x The i-th eigenvalue of j is the autocorrelation matrix R x The jth eigenvalue of is the autocorrelation matrix R x The eigenvalue of the tth iteration, is the autocorrelation matrix R x The eigenvalue of the t+1th iteration, τ3 is a random value generated between 0 and 1; Select the eigenvalue λ that satisfies constraints 1 and 2 i , according to the adaptive solution, the corresponding eigenvalue λ that can be used for subsequent threshold selection can be obtained; the adaptive model formula is as follows: Among them, % is the remainder symbol.

5. The main characteristic breathing and heart rate detection method of millimeter wave radar according to claim 4 is characterized in that: In step 2, based on the mean principal component analysis algorithm, for the eigenvalues ​​obtained by adaptive solution, the respiratory signal amplitude is more than 10 times the heartbeat signal amplitude, and the respiratory signal eigenvector q corresponding to each eigenvalue is obtained respectively. g and the heartbeat signal feature vector q m , the expression formula of eigenvalue to eigenvector is as follows: λ·q m =R x ·q m λ·q g =Rx·q g And the eigenvector q g Arrange them in columns to form the eigenvector matrix Q g ; The feature vector q m Arrange them in columns to form the eigenvector matrix Q m ; So the coefficient vector y of the breathing signal and heartbeat signal at the nth moment is obtained g (n) and y m (n), and g (and g (1)、and g (2)、...、and g (N)) y m = (y m (1), y m (2),..., y m (N)), where n ∈ [1, N] According to the coefficient vector y of the breathing signal and heartbeat signal at the nth moment g (n) and y m (n), the breathing signal at this time can be estimated and heartbeat signal The specific formula is as follows:

6. A millimeter wave radar main feature breathing and heart rate detection system, characterized by: include A first signal acquisition module is used to acquire the chest intermediate frequency signal collected by the millimeter wave radar hardware board and pre-process it to obtain a first signal; A first signal processing module, used for processing the first signal based on a mean principal component analysis algorithm to obtain a respiratory signal and a heartbeat signal; A heart rate acquisition module is used to process the respiratory signal based on the FFT spectrum estimation algorithm to obtain the respiratory rate, and to process the heartbeat signal based on the FFT spectrum estimation algorithm to obtain the heart rate; The specific steps of the pretreatment include: Perform fast Fourier transform on the intermediate frequency signal collected by the millimeter wave radar; The intermediate frequency signal after Fourier transformation is reorganized and the phase signal of the chest position is obtained by using the inverse tangent algorithm; Performing phase differentiation on the obtained phase signal to obtain a first signal; The mean-based principal component analysis algorithm is as follows: Calculating the autocorrelation matrix and covariance matrix of the first signal, and obtaining the eigenvalue of the autocorrelation matrix, and averaging all the obtained eigenvalues ​​of the autocorrelation matrix to obtain an average eigenvalue; The eigenvalue range of the respiratory and heartbeat signals is obtained according to the average eigenvalue, and the eigenvector and eigenvector matrix of the respiratory signal and the eigenvector and eigenvector matrix of the heartbeat signal are determined respectively according to the amplitude characteristic that the amplitude of the respiratory signal is more than ten times that of the heartbeat signal, and then the coefficient vector of the respiratory and heartbeat signals is obtained by solving; The respiratory signal and the heartbeat signal are estimated according to the respiratory and heartbeat signal coefficient vector obtained by solving the solution.

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