Multi-channel fused millimeter wave MIMO radar heartbeat signal detection method and device

Abstract

The application provides a multi-channel fusion millimeter wave MIMO radar heartbeat signal detection method and device, which comprises the following steps: collecting a multi-channel time sequence signal matrix of chest wall movement of a target object through a millimeter wave MIMO radar; obtaining a channel cross-correlation coefficient based on a covariance matrix of the multi-channel time sequence signal matrix and a diagonal matrix of the covariance matrix; obtaining a channel trend correlation coefficient according to a real part data matrix and an imaginary part data matrix of the multi-channel time sequence signal matrix; obtaining inter-channel signal correlation characteristic data according to the channel cross-correlation coefficient and the channel trend correlation coefficient; performing fusion and unwrapping processing on the multi-channel time sequence signal matrix according to a chest wall azimuth angle of the target object when the multi-channel time sequence signal matrix is collected and the inter-channel signal correlation characteristic data to obtain a chest wall physiological signal; and performing variational mode decomposition processing on the chest wall physiological signal to obtain a heartbeat signal of the target object according to an intrinsic mode function component in a preset component range.

Description

Technical Field

[0001] This application relates to the technical field of heartbeat signal detection, specifically to a multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method and apparatus. Background Technology

[0002] Millimeter-wave MIMO radar, with its excellent spatial resolution, can effectively separate human echoes from environmental clutter, thereby enhancing the signal-to-noise ratio of heartbeat signals. Therefore, it has been widely used in non-contact heartbeat detection. However, most existing technologies only utilize the spatial resolution of MIMO radar for target separation, without fully exploiting the array gain and signal diversity advantages brought by its multi-channel reception. How to further improve the signal-to-noise ratio of weak heartbeat signals using multi-channel information remains a pressing technical challenge.

[0003] Currently, existing technologies for processing multi-channel signals typically employ relatively simple strategies: either directly adding the signals from multiple channels (incoherent summation) or directly selecting the optimal single-channel signal for subsequent processing based on signal-to-noise ratio (SNR) evaluation. While these approaches are simple to implement, they neglect the phase correlation and spatial complementarity between multi-channel signals, failing to fully utilize the potential gain provided by MIMO radar multi-channel systems. This results in limited accuracy of heart rate detection in low SNR environments. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings and deficiencies in the prior art and provide a multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method and device, which can accurately collect heartbeat signals from the multi-channel time-series signal matrix acquired by millimeter-wave MIMO radar.

[0005] The first aspect of this application provides a multi-channel fused millimeter-wave MIMO radar heartbeat signal detection method, including: The multi-channel time-series signal matrix of the chest wall motion of the target object is acquired using millimeter-wave MIMO radar. Based on the covariance matrix and the diagonal matrix of the covariance matrix of the multi-channel time-series signal matrix, the channel cross-correlation coefficient is obtained; Based on the real and imaginary data matrices of the multi-channel time-series signal matrix, the channel trend correlation coefficient is obtained; Based on the channel cross-correlation coefficient and the channel trend correlation coefficient, obtain the signal correlation characterization data between channels; Based on the chest wall azimuth of the target object when acquiring the multi-channel time-series signal matrix and the correlation coefficient between the channels, the multi-channel time-series signal matrix is ​​fused and unwrapped to obtain the chest wall physiological signal. The chest wall physiological signals were subjected to variational mode decomposition to obtain intrinsic mode function components; The heartbeat signal of the target object is obtained based on the intrinsic mode function components within a preset component range.

[0006] As one implementation method, the step of obtaining the channel cross-correlation coefficients based on the covariance matrix and the diagonal matrix of the multi-channel time-series signal matrix includes: Obtain the covariance matrix of the multi-channel time-series signal matrix, and form the diagonal matrix based on the diagonal elements of the covariance matrix; The difference matrix is ​​obtained based on the covariance matrix and the diagonal matrix; The first mean vector is obtained by summing the column values ​​of the difference matrix and averaging them. The first mean vector is normalized to obtain the channel cross-correlation coefficient.

[0007] As one implementation method, the covariance matrix is ​​obtained using the following formula: in, Represents the covariance matrix, Represents the number of frames in slow time. Represents a multi-channel time-series signal matrix. Represents the conjugate transpose of a matrix or vector; The difference matrix is ​​obtained using the following formula: in, Represents the difference matrix, Represents the diagonal matrix; The first mean vector is obtained using the following formula: in, Represents the first mean vector. Represents the number of columns in the matrix. Represents the first of the difference matrix List, This represents the modulo operation for complex numbers; The cross-correlation coefficient of the channels can be obtained using the following formula: in, This represents the number of interrelationships between the channels. The first mean vector represents the first mean vector. Each element.

[0008] As one implementation method, the step of obtaining the channel trend correlation coefficient based on the real part data matrix and the imaginary part data matrix of the multi-channel time-series signal matrix includes: Obtain the real part difference data and imaginary part difference data of each row element of the multi-channel time-series signal matrix; Based on the first parameter threshold and the second parameter threshold, characterization analysis is performed on the real part difference data and the imaginary part difference data to obtain an initial real part matrix including the real part characterization data and an initial imaginary part matrix including the imaginary part characterization data. Matrix processing is performed on the initial real part matrix and the initial imaginary part matrix respectively to obtain the real part data matrix and the imaginary part data matrix; The real part data matrix and the imaginary part data matrix are processed by diagonal element processing, addition processing, column summation and averaging to obtain the second mean vector; The second mean vector is normalized to obtain the channel trend correlation coefficient.

[0009] As one implementation method, the real part characterization data is obtained using the following formula: in, The real part represents the data. Represents the first parameter threshold. This represents the second parameter threshold. The first channel of the multi-channel timing signal matrix represents the... The real part difference data of the elements in the row; The imaginary part representation data is obtained using the following formula: in, The imaginary part represents the data. The first channel of the multi-channel timing signal matrix represents the... The imaginary part difference data of the elements in the row; The real part data matrix is ​​obtained using the following formula: in, Represents the real part of the data matrix. Represents the number of rows in the matrix. Represents the initial real part matrix. Represents transposition; The imaginary part data matrix is ​​obtained using the following formula: in, Represents the imaginary part of the data matrix; The second mean vector is obtained using the following function: in, Represents the second mean vector. Represents the number of columns in the matrix. The first element represents the real part of the data matrix after removing the diagonal elements. List, The first element represents the imaginary part of the data matrix after removing the diagonal elements. List; The channel trend correlation coefficient is obtained using the following function: in, This represents the correlation coefficient of the channel trend. The second mean vector represents the first... Each element.

[0010] As one implementation method, the step of obtaining inter-channel signal correlation characterization data based on the channel cross-correlation coefficient and the channel trend correlation coefficient includes: Obtain the difference between the cross-correlation coefficient of the channels and the trend correlation coefficient of the channels; The coefficient differences are normalized to obtain the signal correlation characterization data between the channels.

[0011] As one implementation method, the coefficient difference is obtained using the following formula: in, This represents the difference in the coefficients. This represents the number of interrelationships between the channels. This represents the correlation coefficient of the channel trend; The inter-channel signal correlation characterization data can be obtained using the following formula: in, This represents the data characterizing the signal correlation between the channels. The first representing the inter-channel signal correlation characterization data One element, Represents the number of columns in the matrix.

[0012] As one implementation method, the step of fusing and unwinding the multi-channel time-series signal matrix to obtain chest wall physiological signals, based on the chest wall azimuth angle of the target object when acquiring the multi-channel time-series signal matrix and the correlation coefficient between the channels, includes: The multi-channel time-series signal matrix is ​​fused using the following formula to obtain the fused matrix: in, Represents the fusion matrix, Represents the azimuth angle of the chest wall array manifold vector, Data representing the correlation between signals in different channels. Represents a multi-channel timing signal matrix; The physiological signals of the chest wall are obtained using the following formula: in, This represents the physiological signals of the chest wall. Represents the phase unwinding function. This represents a phase take operation. Represents the real number field. This represents the number of array rows, i.e., the number of slow-time frames for acquiring the multi-channel timing signal matrix.

[0013] As one implementation method, the heartbeat signal of the target object is obtained using the following formula: in, Represents the target object number The heartbeat signal at that moment. Represents the first component within the preset component range The number of eigenmode function components at each time step. Representing the The values ​​of the intrinsic mode function components at time k. Representing the Energy weights for each eigenmode function component: in, Representing the The energy of each eigenmode function component.

[0014] A second aspect of this application provides a multi-channel fused millimeter-wave MIMO radar heartbeat signal detection device, comprising: The multi-channel timing signal acquisition module is used to acquire the multi-channel timing signal matrix of the chest wall motion of the target object through millimeter-wave MIMO radar; The channel cross-correlation coefficient acquisition module is used to acquire the channel cross-correlation coefficient based on the covariance matrix and the diagonal matrix of the covariance matrix of the multi-channel time-series signal matrix. The channel trend correlation coefficient acquisition module is used to acquire the channel trend correlation coefficient based on the real part data matrix and the imaginary part data matrix of the multi-channel time series signal matrix. The inter-channel signal correlation characterization data acquisition module is used to acquire inter-channel signal correlation characterization data based on the channel cross-correlation coefficient and the channel trend correlation coefficient. The chest wall physiological signal acquisition module is used to perform fusion and unwinding processing on the multi-channel time-series signal matrix based on the chest wall azimuth of the target object and the signal correlation characterization number between the channels when acquiring the multi-channel time-series signal matrix, so as to obtain the chest wall physiological signal. The variational mode decomposition processing module is used to perform variational mode decomposition processing on the chest wall physiological signal to obtain intrinsic mode function components; The heartbeat signal acquisition module is used to acquire the heartbeat signal of the target object based on the intrinsic mode function components within a preset component range.

[0015] Compared to related technologies, this application first acquires a multi-channel time-series signal matrix of chest wall motion of a target object using millimeter-wave MIMO radar. Then, based on the covariance matrix and diagonal matrix of the multi-channel time-series signal matrix, it obtains the channel cross-correlation coefficients. Based on the real and imaginary data matrices of the multi-channel time-series signal matrix, it obtains the channel trend correlation coefficients. Next, based on the channel cross-correlation coefficients and the channel trend correlation coefficients, it obtains signal correlation characterization data between channels. Combined with the chest wall azimuth angle of the target object, it performs fusion and unwinding processing on the multi-channel time-series signal matrix to obtain the chest wall physiological signal. Then, it performs variational mode decomposition and heartbeat signal extraction processing on the chest wall physiological signal to obtain the target object's heartbeat signal. In the above process, the embodiments of this application perform multi-channel signal fusion based on the correlation of signals from each channel, which can improve the signal-to-weight ratio of the heartbeat signal, achieve enhancement processing for weak heartbeat signals, and improve the accuracy of the extracted heartbeat signal.

[0016] To provide a clearer understanding of this application, the specific embodiments of this application will be described below in conjunction with the accompanying drawings. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to an embodiment of this application.

[0018] Figure 2 This is a schematic diagram illustrating the case test comparison results of one embodiment of this application.

[0019] Figure 3 This is a schematic diagram of the module connections of a multi-channel fused millimeter-wave MIMO radar heartbeat signal detection device according to an embodiment of this application.

[0020] 100. Heartbeat signal detection device; 101. Multi-channel time-series signal acquisition module; 102. Channel cross-correlation coefficient acquisition module; 103. Channel trend correlation coefficient acquisition module; 104. Channel signal correlation characterization data acquisition module; 105. Chest wall physiological signal acquisition module; 106. Variational mode decomposition processing module; 107. Heartbeat signal acquisition module. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0022] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0023] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."

[0024] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0025] Please see Figure 1 , Figure 1 A flowchart of a multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to a first embodiment of this application is shown. The detection method includes the following steps: S1: Acquire a multi-channel time-series signal matrix of chest wall motion of the target object using millimeter-wave MIMO radar.

[0026] Millimeter-wave MIMO radar transmits millimeter-wave signals through multiple transmitting and receiving antennas and receives the echoes reflected from the target, thus acquiring multi-channel signals. Therefore, the chest wall motion signal of the target object acquired by millimeter-wave MIMO radar can be represented as: ,in, This represents the number of fast-time sampling points (number of sampling points per frame). This represents the total number of transmit and receive antenna channels (for example, the total number of transmit and receive antenna channels for a 3-transmit 4-receive MIMO is 12, and the total number of transmit and receive antenna channels for a 4-transmit 4-receive MIMO is 16). This represents the number of slow-motion frames (one chirp per frame), meaning each frame contains only one chirp signal. A chirp signal is a signal whose frequency changes linearly with time (usually increasing or decreasing linearly). It represents the complex field.

[0027] By performing FFT (Fast Fourier Transform) processing on the fast time dimension, the distance information of the target corresponding to the signal can be obtained, including the chest wall motion signal. The first dimension, namely the fast time dimension, is subjected to the FFT operation as shown in the following formula: .in, Represents distance, The number of transmit and receive antenna channels representing chest wall motion signals. The number of slow-time frames representing the chest wall motion signal.

[0028] Then, using the distance unit corresponding to the chest wall as an index, the corresponding chest wall distance is obtained. Multi-channel timing signal matrix acquired from all transmit and receive channels: S2: Based on the covariance matrix and the diagonal matrix of the covariance matrix of the multi-channel time-series signal matrix, obtain the channel cross-correlation coefficients, including: S21: Obtain the covariance matrix of the multi-channel time-series signal matrix, and construct the diagonal matrix based on the diagonal elements of the covariance matrix: The covariance matrix is ​​obtained using the following formula: in, Represents the covariance matrix, Represents the number of frames in slow time. Represents a multi-channel time-series signal matrix. Represents the conjugate transpose of a matrix or vector.

[0029] S22: Obtain the difference matrix based on the covariance matrix and the diagonal matrix: The difference matrix is ​​obtained using the following formula: in, Represents the difference matrix, This represents the diagonal matrix.

[0030] S23: Sum the moduli of the difference matrix column by column and calculate the average value to obtain the first mean vector; The first mean vector is obtained using the following formula: in, Represents the first mean vector. This represents the number of columns in the matrix (i.e., the total number of transmit and receive antenna channels). Represents the first of the difference matrix List, This represents the modulo operation for complex numbers.

[0031] S24: Normalize the first mean vector to obtain the channel cross-correlation coefficient: The cross-correlation coefficient of the channels can be obtained using the following formula: in, This represents the number of interrelationships between the channels. The first mean vector represents the first mean vector. Each element.

[0032] S3: Based on the real and imaginary data matrices of the multi-channel time-series signal matrix, obtain the channel trend correlation coefficients, including: S31: Obtain the real and imaginary difference data of each row element of the multi-channel time-series signal matrix: Among them, the multi-channel time-series signal matrix The real and imaginary parts of (equivalent to the m-th channel) are respectively and ,and and The first difference is divided into and .

[0033] S32: Based on the first parameter threshold and the second parameter threshold, perform characterization analysis on the real part difference data and the imaginary part difference data to obtain an initial real part matrix including the real part characterization data and an initial imaginary part matrix including the imaginary part characterization data. The real part representation data is obtained using the following formula: in, The real part represents the data. Represents the first parameter threshold. This represents the second parameter threshold. The first channel of the multi-channel timing signal matrix represents the... The real part difference data of the elements in the row.

[0034] Based on all transmit and receive antenna channels The real part representation data can be used to form the initial real part matrix. .

[0035] The imaginary part representation data is obtained using the following formula: in, The imaginary part represents the data. The first channel of the multi-channel timing signal matrix represents the... The imaginary difference data of the elements in the row.

[0036] Based on all transmit and receive antenna channels The imaginary part representation data can be used to form the initial imaginary part matrix. .

[0037] S33: Perform matrix processing on the initial real part matrix and the initial imaginary part matrix respectively to obtain the real part data matrix and the imaginary part data matrix; The real part data matrix is ​​obtained using the following formula: in, Represents the real part of the data matrix. Represents the number of rows in the matrix. Represents the initial real part matrix. This represents transposition.

[0038] The imaginary part data matrix is ​​obtained using the following formula: in, This represents the imaginary part of the data matrix.

[0039] S34: Perform diagonal element processing, addition, column summation, and averaging on the real and imaginary data matrices to obtain a second mean vector: The second mean vector is obtained using the following function: in, Represents the second mean vector. Represents the number of columns in the matrix. The first element represents the real part of the data matrix after removing the diagonal elements. List, The first element represents the imaginary part of the data matrix after removing the diagonal elements. List.

[0040] S35: Normalize the second mean vector to obtain the channel trend correlation coefficient: The channel trend correlation coefficient is obtained using the following function: in, This represents the correlation coefficient of the channel trend. The second mean vector represents the first... Each element.

[0041] S4: Based on the channel cross-correlation coefficient and the channel trend correlation coefficient, obtain the signal correlation characterization data between channels, including: S41: Obtain the difference between the cross-correlation coefficient of the channels and the trend correlation coefficient of the channels: The coefficient difference is obtained using the following formula: in, This represents the difference in the coefficients. This represents the number of interrelationships between the channels. This represents the correlation coefficient of the channel trend.

[0042] S42: Normalize the coefficient difference to obtain the signal correlation characterization data between channels: The inter-channel signal correlation characterization data can be obtained using the following formula: in, This represents the data characterizing the signal correlation between the channels. The first representing the inter-channel signal correlation characterization data One element, Represents the number of columns in the matrix.

[0043] S5: Based on the chest wall azimuth of the target object and the correlation coefficient between the channels when acquiring the multi-channel time-series signal matrix, the multi-channel time-series signal matrix is ​​fused and unwrapped to obtain chest wall physiological signals, including: The multi-channel time-series signal matrix is ​​fused using the following formula to obtain the fused matrix: in, Represents the fusion matrix, Represents the azimuth angle of the chest wall array manifold vector, Data representing the correlation between signals in different channels. Represents a multi-channel time-series signal matrix. Represents the dot product operation; The physiological signals of the chest wall are obtained using the following formula: in, This represents the physiological signals of the chest wall. Represents the phase unwinding function. This represents a phase take operation. Represents the real number field. This represents the number of array rows, i.e., the number of slow-time frames for acquiring the multi-channel timing signal matrix.

[0044] The chest wall azimuth angle of the target object when acquiring multi-channel time-series signal matrices can be obtained using super-resolution algorithms such as MUSIC, CAPON, and sparse recovery. Taking the MUSIC algorithm as an example, the MUSIC spatial spectrum is shown in the following formula: in, Represents the corresponding chest wall azimuth angle The MUSIC spatial spectrum ; Representing the noise subspace, it is determined by the covariance matrix. Eigenvalue decomposition is performed, and the corresponding eigenvectors are selected to obtain the results. Represents the conjugate transpose of a matrix or vector.

[0045] S6: Perform variational mode decomposition on the chest wall physiological signals to obtain intrinsic mode function components: For example, using the VMD algorithm to perform variational mode decomposition on chest wall physiological signals, with parameters selected as 10 layers and a penalty factor of 5000, the intrinsic mode function components (IMF components) can be obtained. The IMF components in the VMD algorithm are a set of sub-signals with finite bandwidth and definite center frequencies obtained by Variational Mode Decomposition (VMD) from the original signal. Each IMF component represents an oscillation mode at a specific frequency scale in the original signal.

[0046] S7: Obtain the heartbeat signal of the target object based on the intrinsic mode function components within the preset component range: The heartbeat signal of the target object can be obtained using the following formula: in, Represents the target object number The heartbeat signal at that moment. Represents the first component within the preset component range The number of eigenmode function components at each time step. Representing the The values ​​of the intrinsic mode function components at time k. Representing the Energy weights for each eigenmode function component: in, Representing the The energy of each eigenmode function component.

[0047] Therefore, the heartbeat signal of the target object can be represented as .

[0048] Compared to related technologies, the heartbeat signal detection method in this embodiment first acquires a multi-channel time-series signal matrix of chest wall motion of the target object using millimeter-wave MIMO radar. Then, based on the covariance matrix and diagonal matrix of the multi-channel time-series signal matrix, the channel cross-correlation coefficient is obtained. According to the real and imaginary data matrices of the multi-channel time-series signal matrix, the channel trend correlation coefficient is obtained. Then, based on the channel cross-correlation coefficient and the channel trend correlation coefficient, the signal correlation characterization data between channels is obtained. Combined with the chest wall azimuth angle of the target object, the multi-channel time-series signal matrix is ​​fused and unwrapped to obtain the chest wall physiological signal. Then, variational mode decomposition and heartbeat signal extraction are performed on the chest wall physiological signal to obtain the target object's heartbeat signal. It can be understood that in the above process, multi-channel signal fusion based on the correlation of each channel signal can improve the signal-to-weight ratio of the heartbeat signal, achieve enhancement processing of weak heartbeat signals, and help improve the accuracy of the extracted heartbeat signal.

[0049] Specifically, to further demonstrate the technical effect of this embodiment, a 77GHz 3-transmit 4-receive (12 channels in total) MIMO radar was used to acquire data. The fast time dimension sampling points were 256, the fast time sampling rate was 5MHz, the slow time dimension sampling frequency was 1000Hz, the duration was approximately 60s, the signal modulation frequency was 66.626MHz / microsecond, and the bandwidth was 3.997GHz. The test subject sat approximately 65cm in front of the radar. The first parameter threshold... The second parameter threshold Angle range The sample was discretized into 180 grid points and compared with the maximum signal-to-noise ratio method (i.e., selecting the channel with the highest signal-to-noise ratio out of 12 channels and using only that channel to extract the heartbeat signal).

[0050] To ensure objectivity, all other processing procedures remained unchanged in the experiment, and the results are as follows: Figure 2 As shown. Figure 2 This diagram illustrates the comparison results of the case tests in this embodiment. During the comparison, the commonly used metric, Mean Absolute Error Percentage (AAEP), is used to measure the error between the radar measurement value and the ECG (electrocardiogram) reference value. The formula for calculating AAEP is: In the above formula, Represents the percentage of mean absolute error. Represents the total number of heartbeats. Represents the radar acquisition of the first Second estimate, This represents the corresponding ECG reference value.

[0051] The AAEP values ​​for the two methods are shown in Table 1 below.

[0052] Table 1: From Table 1 and Figure 2 It can be seen that the heartbeat signal error detected in this embodiment is much smaller than that detected using the maximum signal-to-noise ratio method. That is, this embodiment can accurately collect the heartbeat signal from the multi-channel time-series signal matrix acquired by the millimeter-wave MIMO radar.

[0053] Please see Figure 3 , Figure 3 A schematic diagram of the module connections for the multi-channel fused millimeter-wave MIMO radar heartbeat signal detection device of this embodiment is shown. A second embodiment of this application provides a multi-channel fused millimeter-wave MIMO radar heartbeat signal detection device 100, which includes: The multi-channel timing signal acquisition module 101 is used to acquire the multi-channel timing signal matrix of the chest wall motion of the target object through millimeter-wave MIMO radar; The channel cross-correlation coefficient acquisition module 102 is used to acquire the channel cross-correlation coefficient based on the covariance matrix and the diagonal matrix of the covariance matrix of the multi-channel time-series signal matrix. The channel trend correlation coefficient acquisition module 103 is used to acquire the channel trend correlation coefficient based on the real part data matrix and the imaginary part data matrix of the multi-channel time series signal matrix. The channel signal correlation characterization data acquisition module 104 is used to acquire channel signal correlation characterization data based on the channel cross-correlation coefficient and the channel trend correlation coefficient. The chest wall physiological signal acquisition module 105 is used to perform fusion processing and unwinding processing on the multi-channel time-series signal matrix based on the chest wall azimuth angle of the target object when acquiring the multi-channel time-series signal matrix and the signal correlation characterization number between the channels, so as to obtain the chest wall physiological signal. The variational mode decomposition processing module 106 is used to perform variational mode decomposition processing on the chest wall physiological signal to obtain intrinsic mode function components. The heartbeat signal acquisition module 107 is used to acquire the heartbeat signal of the target object based on the intrinsic mode function components within a preset component range.

[0054] It should be noted that the heartbeat signal detection device 100 provided in the second embodiment of this application is only illustrated by the above-described division of functional modules when performing the heartbeat signal detection method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the heartbeat signal detection device 100 provided in the second embodiment of this application and the heartbeat signal detection method in the first embodiment of this application belong to the same concept, and its implementation process is detailed in the method embodiment, which will not be repeated here.

[0055] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and 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 application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0056] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function selected in one or more boxes.

[0058] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.

[0059] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0060] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0061] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0062] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0063] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A multi-channel fusion method for detecting heartbeat signals in millimeter-wave MIMO radar, characterized in that, include: The multi-channel time-series signal matrix of the chest wall motion of the target object is acquired using millimeter-wave MIMO radar. Based on the covariance matrix and the diagonal matrix of the covariance matrix of the multi-channel time-series signal matrix, the channel cross-correlation coefficient is obtained; Based on the real and imaginary data matrices of the multi-channel time-series signal matrix, the channel trend correlation coefficient is obtained; Based on the channel cross-correlation coefficient and the channel trend correlation coefficient, obtain the signal correlation characterization data between channels; Based on the chest wall azimuth of the target object when acquiring the multi-channel time-series signal matrix and the correlation coefficient between the channels, the multi-channel time-series signal matrix is ​​fused and unwrapped to obtain the chest wall physiological signal. The chest wall physiological signals were subjected to variational mode decomposition to obtain intrinsic mode function components; The heartbeat signal of the target object is obtained based on the intrinsic mode function components within the preset component range. The step of obtaining the channel cross-correlation coefficients based on the covariance matrix and the diagonal matrix of the multi-channel time-series signal matrix includes: Obtain the covariance matrix of the multi-channel time-series signal matrix, and form the diagonal matrix based on the diagonal elements of the covariance matrix; The difference matrix is ​​obtained based on the covariance matrix and the diagonal matrix; The first mean vector is obtained by summing the column values ​​of the difference matrix and averaging them. The first mean vector is normalized to obtain the channel cross-correlation coefficient; The step of obtaining the channel trend correlation coefficient based on the real part data matrix and the imaginary part data matrix of the multi-channel time-series signal matrix includes: Obtain the real part difference data and imaginary part difference data of each row element of the multi-channel time-series signal matrix; Based on the first parameter threshold and the second parameter threshold, characterization analysis is performed on the real part difference data and the imaginary part difference data to obtain an initial real part matrix including the real part characterization data and an initial imaginary part matrix including the imaginary part characterization data. Matrix processing is performed on the initial real part matrix and the initial imaginary part matrix respectively to obtain the real part data matrix and the imaginary part data matrix; The real part data matrix and the imaginary part data matrix are processed by diagonal element processing, addition processing, column summation and averaging to obtain the second mean vector; The second mean vector is normalized to obtain the channel trend correlation coefficient.

2. The multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to claim 1, characterized in that: The covariance matrix is ​​obtained using the following formula: ； in, Represents the covariance matrix, Represents the number of frames in slow time. Represents a multi-channel time-series signal matrix. Represents the conjugate transpose of a matrix or vector; The difference matrix is ​​obtained using the following formula: ； in, Represents the difference matrix, Represents the diagonal matrix; The first mean vector is obtained using the following formula: ； in, Represents the first mean vector. Represents the number of columns in the matrix. Represents the first of the difference matrix List, Table of complex number modulo operations; The cross-correlation coefficient of the channels can be obtained using the following formula: ； in, This represents the number of interrelationships between the channels. The first mean vector represents the first mean vector. Each element.

3. The multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to claim 1, characterized in that: The real part representation data is obtained using the following formula: ； in, The real part represents the data. Represents the first parameter threshold. This represents the second parameter threshold. The first channel of the multi-channel timing signal matrix represents the... The real part difference data of the elements in the row; The imaginary part representation data is obtained using the following formula: ； in, The imaginary part represents the data. The first channel of the multi-channel timing signal matrix represents the... The imaginary part difference data of the elements in the row; The real part data matrix is ​​obtained using the following formula: ； in, Represents the real part of the data matrix. Represents the number of rows in the matrix. Represents the initial real part matrix. Represents transposition; The imaginary part data matrix is ​​obtained using the following formula: ； in, Represents the imaginary part of the data matrix; The second mean vector is obtained using the following function: ； in, Represents the second mean vector. Represents the number of columns in the matrix. The first element represents the real part of the data matrix after removing the diagonal elements. List, The first element represents the imaginary part of the data matrix after removing the diagonal elements. List; The channel trend correlation coefficient is obtained using the following function: ； in, This represents the correlation coefficient of the channel trend. The second mean vector represents the first... Each element.

4. The multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to claim 1, characterized in that, The step of obtaining signal correlation characterization data between channels based on the channel cross-correlation coefficient and the channel trend correlation coefficient includes: Obtain the difference between the cross-correlation coefficient of the channels and the trend correlation coefficient of the channels; The coefficient differences are normalized to obtain the signal correlation characterization data between the channels.

5. The multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to claim 4, characterized in that: The coefficient difference is obtained using the following formula: ； in, This represents the difference in the coefficients. This represents the number of interrelationships between the channels. This represents the correlation coefficient of the channel trend; The inter-channel signal correlation characterization data can be obtained using the following formula: ； in, This represents the data characterizing the signal correlation between the channels. The first representing the inter-channel signal correlation characterization data One element, Represents the number of columns in the matrix.

6. The multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to claim 1, characterized in that, The steps for obtaining chest wall physiological signals by fusing and unwinding the multi-channel time-series signal matrix based on the chest wall azimuth of the target object during the acquisition of the multi-channel time-series signal matrix and the correlation coefficient between the channels include: The multi-channel time-series signal matrix is ​​fused using the following formula to obtain the fused matrix: ； in, Represents the fusion matrix, Represents the azimuth angle of the chest wall array manifold vector, Data representing the correlation between signals in different channels. Represents a multi-channel timing signal matrix; The physiological signals of the chest wall are obtained using the following formula: ； in, This represents the physiological signals of the chest wall. Represents the phase unwinding function. This represents a phase take operation. Represents the real number field. This represents the number of array rows, i.e., the number of slow-time frames for acquiring the multi-channel timing signal matrix.

7. The multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection method according to claim 1, characterized in that, The heartbeat signal of the target object can be obtained using the following formula: ； in, Represents the target object number The heartbeat signal at that moment. Represents the first component within the preset component range The number of eigenmode function components at each time step. Representing the The values ​​of the intrinsic mode function components at time k. Representing the Energy weights for each eigenmode function component: ； in, Representing the The energy of each eigenmode function component.

8. A multi-channel fusion millimeter-wave MIMO radar heartbeat signal detection device, characterized in that, include: The multi-channel timing signal acquisition module is used to acquire the multi-channel timing signal matrix of the chest wall motion of the target object through millimeter-wave MIMO radar; The channel cross-correlation coefficient acquisition module is used to acquire channel cross-correlation coefficients based on the covariance matrix and the diagonal matrix of the covariance matrix of the multi-channel time-series signal matrix, including: Obtain the covariance matrix of the multi-channel time-series signal matrix, and form the diagonal matrix based on the diagonal elements of the covariance matrix; The difference matrix is ​​obtained based on the covariance matrix and the diagonal matrix; The first mean vector is obtained by summing the column values ​​of the difference matrix and averaging them. The first mean vector is normalized to obtain the channel cross-correlation coefficient; The channel trend correlation coefficient acquisition module is used to acquire the channel trend correlation coefficient based on the real part data matrix and the imaginary part data matrix of the multi-channel time-series signal matrix, including: Obtain the real part difference data and imaginary part difference data of each row element of the multi-channel time-series signal matrix; Based on the first parameter threshold and the second parameter threshold, characterization analysis is performed on the real part difference data and the imaginary part difference data to obtain an initial real part matrix including the real part characterization data and an initial imaginary part matrix including the imaginary part characterization data. Matrix processing is performed on the initial real part matrix and the initial imaginary part matrix respectively to obtain the real part data matrix and the imaginary part data matrix; The real part data matrix and the imaginary part data matrix are processed by diagonal element processing, addition processing, column summation and averaging to obtain the second mean vector; The second mean vector is normalized to obtain the channel trend correlation coefficient; The inter-channel signal correlation characterization data acquisition module is used to acquire inter-channel signal correlation characterization data based on the channel cross-correlation coefficient and the channel trend correlation coefficient. The chest wall physiological signal acquisition module is used to perform fusion and unwinding processing on the multi-channel time-series signal matrix based on the chest wall azimuth of the target object and the signal correlation characterization number between the channels when acquiring the multi-channel time-series signal matrix, so as to obtain the chest wall physiological signal. The variational mode decomposition processing module is used to perform variational mode decomposition processing on the chest wall physiological signal to obtain intrinsic mode function components; The heartbeat signal acquisition module is used to acquire the heartbeat signal of the target object based on the intrinsic mode function components within a preset component range.

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