Millimeter wave radar human vital sign monitoring method and system based on diversity mechanism

Abstract

The application discloses a millimeter wave radar human vital sign monitoring method and system based on a diversity mechanism, which comprises a millimeter wave radar having a plurality of transmitting antennas for transmitting millimeter wave signals to the environment and a plurality of receiving antennas for receiving signals reflected by the human body and objects; and a signal processing module for mixing the reflected signals received by the receiving antennas, implementing spatial diversity at the position of the human body to obtain the reflected components of the human body position, estimating echo static components to extract dynamic signals, obtaining dynamic components, implementing time diversity on the echo dynamic components at multiple time points to improve the signal-to-noise ratio, extracting phases and unwrapping the signals after time diversity, performing band-pass filtering and cycle estimation in the time domain to obtain the respiration rate and heart rate of the target. The application utilizes the diversity method to improve the human vital sign monitoring accuracy in a complex environment and at a long distance by comprehensively using the data of multiple antennas and multiple pulses.

Description

Technical Field

[0001] This invention relates to the field of millimeter-wave radar technology, and in particular to a method and system for monitoring human vital signs using millimeter-wave radar based on diversity mechanisms. Background Technology

[0002] With the increasing prevalence of millimeter-wave radar, its application in human vital sign monitoring has broad prospects. In the medical field, millimeter-wave radar can be used for non-contact vital sign monitoring; in the smart home field, it can be used to monitor the vital signs of family members; in addition, millimeter-wave radar for human vital sign monitoring also has good application prospects in disaster relief and rescue. How to use millimeter-wave radar to stably monitor human vital signs is a key issue in the field of intelligent wireless sensing today. Currently, the bandwidth of industrial and automotive radars can typically reach 4-5 GHz, and each chip is usually equipped with multiple transmit and receive channels. High bandwidth and multiple channels can bring more refined spatial resolution, which provides good physical support for high-performance human vital sign monitoring using millimeter-wave radar.

[0003] Monitoring human vital signs using millimeter-wave radar involves measuring minute movements of the chest. Existing millimeter-wave radar-based methods for monitoring human vital signs mostly extract phase data based on the distance to the human body, then obtain vital sign data through phase dewinding, filtering, and time / frequency domain period estimation. This method can achieve good monitoring results in simple environments and under close-range conditions.

[0004] However, in practical applications, when using millimeter-wave radar to monitor human vital signs, the environment in which the human body is located is usually quite complex. The environment may contain static objects with high reflectivity. The signals reflected by these static objects are superimposed on the signals reflected from the human chest and received by the radar. The resulting waveform after simply extracting the phase from the distance to the human body is the result of the interaction between these two reflected signals. The static reflection component often affects the extracted waveform and can even cause waveform distortion, severely impacting the monitoring of human vital signs. Furthermore, when the human body is far from the radar, the signal strength reflected by the body is low. In this case, the vital sign information obtained through phase extraction will contain a large amount of noise, which will also affect the monitoring of vital signs.

[0005] Therefore, how to conduct robust monitoring of human vital signs in complex environments and at long distances is an urgent problem to be solved. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a millimeter-wave radar method and system for monitoring human vital signs based on diversity mechanisms. It utilizes dynamic component extraction to suppress static interference and improve the accuracy of monitoring human vital signs in complex environments. Furthermore, it employs diversity methods to enhance the accuracy of monitoring human vital signs at long distances by integrating data from multiple antennas and multiple pulses.

[0007] Therefore, the present invention provides the following technical solution:

[0008] On one hand, this invention provides a method for monitoring human vital signs using millimeter-wave radar based on diversity mechanisms. The millimeter-wave radar has several transmitting antennas and several receiving antennas; the transmitting antennas are used to transmit millimeter-wave signals to the environment; the receiving antennas are used to receive signals reflected by the human body and objects; the method includes:

[0009] The signals received by several receiving antennas after being reflected by the human body and objects are mixed.

[0010] Spatial diversity is performed using the location of the human body to obtain the reflection component of the human body's location;

[0011] Dynamic components are obtained by estimating the static components of the echo and extracting the dynamic signal.

[0012] Time diversity is achieved by utilizing the dynamic components of echoes at multiple times to improve the signal-to-noise ratio;

[0013] Phase extraction and dewinding are performed on the time-diversified signal, followed by bandpass filtering and time-domain period estimation to obtain the target's respiratory rate and heart rate.

[0014] Furthermore, the spatial diversity includes:

[0015] Let X N×K×M×L This is the radar data matrix before spatial diversity processing, where the element with coordinates (n,k,m,l) is x. (n,k,m,l) ; This is the radar data matrix after spatial diversity processing, where the element at coordinates (n,k,m) is...

[0016] The weight vector is calculated based on information such as the transmitted signal wavelength, antenna array element arrangement, the angle to be measured, and the autocorrelation matrix of the received signal.

[0017] For X N×K×M×L Using the weight vector w S Weighted summation is performed to obtain the echo matrix after spatial diversity processing. As shown in the following formula:

[0018]

[0019] in For w S The l-th element.

[0020] Furthermore, the time diversity includes:

[0021] set up This is the radar data matrix before time diversity processing, where the elements at coordinates (n, k, m) are... This is the radar data matrix after time diversity processing, where the element at coordinate (n, m) is...

[0022] Calculate the weight vector based on the number K of pulses in each frame.

[0023] right Using the weight vector w T We perform a weighted summation to obtain the echo vector after time diversity processing. As shown in the following formula:

[0024]

[0025] in For w T The k-th element.

[0026] Furthermore, the dynamic signal extraction includes:

[0027] Enhanced human body position echo Perform an N-point FFT along the first dimension to obtain the distance spectrum R of the echo at the angle of the human body. N×M ;

[0028] Using the distance d between the human body and the radar and the range resolution d res Calculate the distance cell number n where the human body is located:

[0029]

[0030] in Indicates rounding down;

[0031] R N×M All elements in the nth row are denoted as r = [r1, r2, ..., r]. M ], calculate the static component of the echo at its center r0 as the distance unit where the human body is located;

[0032] The dynamic component is calculated using r and the static component r0 of the echo at the distance cell where the human body is located. in The calculation formula is:

[0033]

[0034] In the formula r m This represents the m-th element in r. express The m-th element.

[0035] Furthermore, the time-domain period estimation method is as follows:

[0036] Search the filtered vital sign waveform vector y = [y1, y2, ..., y M The maximum value in the vector [j1, j2, ..., jn] is recorded, and the index of the element corresponding to the maximum value is stored in the vector j, j = [j1, j2, ..., jn]. P ], where j P Let y be the coordinates of the p-th local maximum, and P be the number of local maximums in y.

[0037] Performing a first-order difference on j yields in, p = 1, 2, ..., P-1;

[0038] calculate The average value j of all elements av The respiratory rate or heart rate F of human vital signs is calculated by the following formula:

[0039]

[0040] Where f is the frame frequency of the millimeter-wave radar.

[0041] In another aspect, the present invention also provides a millimeter-wave radar human vital signs monitoring system based on diversity mechanism, the system comprising:

[0042] A millimeter-wave radar having several transmitting antennas and several receiving antennas; the transmitting antennas are used to transmit millimeter-wave signals to the environment; the receiving antennas are used to receive signals reflected by the human body and objects;

[0043] The signal processing module is used to mix the reflected signal received by the receiving antenna, perform spatial diversity using the location of the human body to obtain the reflected component of the human body's position, extract the dynamic signal by estimating the static component of the echo to obtain the dynamic component, perform time diversity using the dynamic components of the echo at multiple times to improve the signal-to-noise ratio, perform phase extraction and dewinding on the time-diversified signal, and perform bandpass filtering and time-domain period estimation to obtain the target's respiratory rate and heart rate.

[0044] Advantages and positive effects of the present invention:

[0045] To address the problem of static object echo interference, this invention proposes a dynamic signal extraction method to suppress interference from static signals and improve the accuracy of human vital sign monitoring in complex environments. To address the issue of low human echo intensity at long distances, this invention utilizes diversity methods, integrating data from multiple antennas and multiple pulses to solve this problem, thereby improving the accuracy of human vital sign monitoring at long distances. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the workflow of a millimeter-wave radar human vital signs monitoring system based on diversity mechanism in this invention embodiment.

[0048] Figure 2 This is a flowchart of the signal processing in an embodiment of the present invention;

[0049] Figure 3 This is a diagram showing the arrangement of antenna array elements in an embodiment of the present invention. Detailed Implementation

[0050] Technical Terminology Explanation:

[0051] FMCW: Frequency Modulated Continuous Wave;

[0052] FFT: Fast Fourier Transform;

[0053] TDM: Time-Division Multiplexing;

[0054] MVDR: Minimum Variance Distortionless Response.

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] This invention discloses a millimeter-wave radar human vital signs monitoring system based on diversity mechanism. The system includes: a millimeter-wave radar with several transmitting antennas and several receiving antennas; the transmitting antennas are used to transmit millimeter-wave signals to the environment; the receiving antennas are used to receive signals reflected by the human body and objects; a signal processing module is used to mix the reflected signals received by the receiving antennas, and to perform spatial diversity using the location of the human body to obtain the reflected components of the human body's location, to extract dynamic signals by estimating the static components of the echo to obtain dynamic components, to perform time diversity using the dynamic components of the echo at multiple times to improve the signal-to-noise ratio, to perform phase extraction and dewinding on the time-diversified signals, and to perform bandpass filtering and time-domain period estimation to obtain the target's respiratory rate and heart rate.

[0058] In this invention, for the problem of monitoring human vital signs in complex environments, dynamic signals are extracted to suppress interference from static signals, thereby improving the accuracy of monitoring human vital signs in complex environments. For the problem of low human echo intensity (i.e., low human echo signal-to-noise ratio) at long distances, diversity methods are used to enhance the signal-to-noise ratio of human signals at long distances, thereby improving the accuracy of monitoring human vital signs at long distances.

[0059] like Figure 1-2 As shown, the specific steps of the signal processing flow in the above system are as follows:

[0060] S101: The millimeter-wave radar continuously transmits M frames of FMCW pulses, with K pulses per frame. After being reflected by people and other debris in the environment, the signals are received by the L receiving antennas of the millimeter-wave radar.

[0061] S102. Mix the transmitted signal and the received signal to obtain the intermediate frequency signal;

[0062] S103. Sample all intermediate frequency signals of the M frames of pulses received by the L receiving antennas at a sampling rate f. sBy sampling at N points, a four-dimensional matrix X of radar echo data is obtained. N×K×M×L The four dimensions are sampling point number, intra-frame pulse number, frame number, and antenna number, respectively.

[0063] S104, Regarding X N×K×M×L Spatial diversity processing is applied along the antenna dimension to obtain After that The time diversity processing of the mid-frame pulse dimension is applied to obtain the enhanced echo at the angle of the human body.

[0064] S105, Echo at the angle of the enhanced human body Dynamic signal extraction is performed to obtain the dynamic components of the echo.

[0065] S106, Dynamic components of echo Phase extraction and phase dewinding are performed to obtain the human vital signs signal y;

[0066] S107. Bandpass filtering is applied to the human vital signs signal to obtain the human respiratory signal y. r and heartbeat signal y h ;

[0067] S108. The respiratory waveform y is monitored at regular intervals. r and heartbeat waveform y h The human respiratory rate F is obtained using a time-domain period estimation method. r and heart rate F h .

[0068] The spatial diversity processing method is as follows:

[0069] S201, Let X N×K×M×L This is the radar data matrix before spatial diversity processing, where the element with coordinates (n,k,m,l) is x. (n,k,m,l) ; This is the radar data matrix after spatial diversity processing, where the element at coordinates (n,k,m) is...

[0070] S202. Calculate the weight vector based on information such as the transmitted signal wavelength, antenna array element arrangement, the angle to be measured, and the autocorrelation matrix of the received signal.

[0071] S203, Regarding X N×K×M×L Using the weight vector w S Weighted summation is performed to obtain the echo matrix after spatial diversity processing. As shown in the following formula

[0072]

[0073] in For w S The l-th element.

[0074] The time diversity processing method is as follows:

[0075] S301, Set This is the radar data matrix before time diversity processing, where the element at coordinates (n,k,n) is... This is the radar data matrix after time diversity processing, where the element at coordinate (n,n) is...

[0076] S302. Calculate the weight vector based on the number of pulses K in each frame.

[0077] S303, to Using the weight vector W T We perform a weighted summation to obtain the echo vector after time diversity processing. As shown in the following formula

[0078]

[0079] in For W T The k-th element.

[0080] The specific steps of the dynamic signal extraction method are as follows:

[0081] S401, Let the enhanced human body position echo be... right Perform an N-point FFT along the first dimension to obtain the distance spectrum R of the echo at the angle of the human body. N×M ;

[0082] S402, Utilizing the distance d between the human body and the radar and the range resolution d res Calculate the distance unit number n where the human body is located, as shown in the following formula.

[0083] in Indicates rounding down;

[0084] S403, R N×M All elements in the nth row are denoted as r = [r1, r2, ..., r]. M ], calculate the static component of the echo at its center r0 as the distance unit where the human body is located;

[0085] S404. Calculate the dynamic component using r and the static component r0 of the echo from the distance cell where the human body is located. in The calculation formula is as follows:

[0086]

[0087] In the formula r m This represents the m-th element in r. express The m-th element.

[0088] The time-domain period estimation method is as follows:

[0089] S501. Let the filtered vital signs waveform vector be y = [y1, y2, ..., y]. M Search for the maximum value in y (excluding the two extremes), record the index of the element corresponding to the maximum value, and store it in vector j, j = [j1, j2, ..., jj]. P ], where j P Let y be the coordinates of the p-th local maximum, and P be the number of local maximums in y.

[0090] S502, Perform a first-order difference on j to obtain As shown in the following formula:

[0091]

[0092] Where p = 1, 2, ..., P-1;

[0093] S503, Calculation The average of all elements in the matrix, denoted as j. av If the frame frequency of millimeter-wave radar is f, then the respiratory rate or heart rate F of human vital signs can be calculated by the following formula:

[0094]

[0095] For ease of understanding, the following section uses a 3-transmitter, 4-receiver 60GHz millimeter-wave radar real-time monitoring system for human vital signs as an example to provide a detailed explanation of the millimeter-wave radar human vital signs monitoring method and system based on diversity mechanism in this invention.

[0096] The system configuration is as follows:

[0097] The system operates in the 60GHz to 64GHz range and uses the FMCW standard;

[0098] The system has 3 transmitting antennas and 4 receiving antennas, and the antenna array elements are arranged as follows: Figure 3 As shown;

[0099] The system has a sampling rate of 5000 ksps for the intermediate frequency signal, uses orthogonal sampling, has 256 sampling points per linear frequency modulation (LFM) pulse, and has an LFM pulse period of 210 µs; each frame contains 64 LFM pulses with a frame period of 50 ms; the transmitting antenna uses TDM (Transmitter Directional Modulation) to transmit the signal.

[0100] The respiratory bandpass filter uses a Butterworth filter with a sampling rate of 20Hz, a passband range of 0.1Hz to 0.5Hz, and an order of 4. The heartbeat bandpass filter uses a Butterworth filter with a sampling rate of 20Hz, a passband range of 0.8Hz to 2Hz, and an order of 8.

[0101] The system performs time-domain periodic estimation every 1 second to obtain vital signs such as respiratory rate and heart rate.

[0102] Task: Use millimeter-wave radar to monitor human respiratory rate and heart rate.

[0103] In this embodiment, the radar received signal is preprocessed, spatial diversity and temporal diversity are performed using the human body position, and the dynamic signal of the echo is extracted. The obtained dynamic components are then subjected to phase extraction, dewinding, and bandpass filtering. Finally, the human respiratory rate and heart rate are obtained through time-domain period estimation.

[0104] The workflow of the millimeter-wave radar human vital signs monitoring system based on diversity mechanism of this invention is as follows:

[0105] The S601 and millimeter-wave radar have three transmitting antennas that continuously transmit FMCW pulses in TDM mode, with 64 pulses per frame. After being reflected by people and other debris in the environment, the signals are received by the millimeter-wave radar's four receiving antennas. The three transmitting and four receiving antennas are equivalent to 12 receiving antennas.

[0106] S602. Mix the transmitted signal and the received signal to obtain the intermediate frequency signal;

[0107] S603. Sample all intermediate frequency signals of M frames of pulses received by L receiving antennas at a sampling rate of 5000 ksps using 256 points. After the number of transmitted frames is greater than or equal to 400 frames, start storing the data of the last 400 frames to obtain the four-dimensional matrix X of radar echo data. 256×64×400×12 The four dimensions are sampling point number, intra-frame pulse number, frame number, and antenna number, respectively.

[0108] S604, Regarding X 256×64×400×12 Spatial diversity processing is applied along the antenna dimension to obtain After that Time diversity processing is applied along the frame pulse dimension to obtain the enhanced echo at the angle of the human body.

[0109] S605, Echo at the angle of the enhanced human body Dynamic signal extraction is performed to obtain the dynamic components of the echo.

[0110] S606, Echo dynamic component Phase extraction and phase dewinding are performed to obtain the human vital signs signal y;

[0111] S607. Bandpass filtering is applied to the human vital signs signal to obtain the human respiratory signal y. r and heartbeat signal y h ;

[0112] S608, adjust the respiratory waveform y every 1 second. r and heartbeat waveform y h The human respiratory rate F is obtained using a time-domain period estimation method. r and heart rate F h .

[0113] The echo spatial diversity processing method in this embodiment of the invention is as follows:

[0114] Let X 256×64×400×12 This is the radar data matrix before spatial diversity processing, where the element with coordinates (n,k,m,l) is x. (n,k,m,l) ; This is the radar data matrix after spatial diversity processing, where the element at coordinates (n,k,m) is...

[0115] The weight vector is calculated using the MVDR criterion based on information such as the transmitted signal wavelength, antenna array element arrangement, the angle to be measured, and the autocorrelation matrix of the received signal.

[0116] For X 256×64×400×12 Using the weight vector w S Weighted summation is performed to obtain the echo matrix after spatial diversity processing. As shown in the following formula:

[0117]

[0118] in For w S The l-th element.

[0119] The echo time diversity processing method in this embodiment of the invention is as follows:

[0120] set up This is the radar data matrix before time diversity processing, where the element at coordinates (n,k,m) is... This is the radar data matrix after time diversity processing, where the element at coordinate (n, m) is...

[0121] The weight vector w is calculated based on the number of pulses per frame (64). T = [1 / 64, 1 / 64, ..., 1 / 64;

[0122] right Using the weight vector w T We perform a weighted summation to obtain the echo vector after time diversity processing. As shown in the following formula:

[0123]

[0124] in For w T The k-th element.

[0125] The specific steps of the dynamic signal extraction method in this embodiment of the invention are as follows:

[0126] Let the enhanced human body position echo be right Perform an N-point FFT along the first dimension to obtain the distance spectrum R of the echo at the angle of the human body. 256×400 ;

[0127] Using the distance d between the human body and the radar and the range resolution d res The distance cell number n where the human body is located is calculated as shown in the following formula:

[0128]

[0129] in Indicates rounding down;

[0130] R 256×400 The nth row is denoted as r = [r1, r2, ..., r 400 The center r0 of the human body is calculated using the least squares circle fitting method as the static component of the echo of the distance unit.

[0131] The dynamic component is calculated using r and the static component r0 of the echo from the distance cell where the human body is located. in The calculation formula is as follows:

[0132]

[0133] In the formula r m This represents the m-th element in r. express The m-th element.

[0134] The time-domain period estimation method in this embodiment of the invention is as follows:

[0135] Let the filtered vital sign waveform vector be y = [y1, y2, ..., y]. 400 Search for the maximum value in y (excluding the two extremes), record the index of the element corresponding to the maximum value, and store it in vector j, j = [j1, j2, ..., jj]. P ], where j P Let y be the coordinates of the p-th local maximum, and P be the number of local maximums in y.

[0136] Performing a first-order difference on j yields As shown in the following formula:

[0137]

[0138] Where p = 1, 2, ..., 399;

[0139] calculate The average of all elements in the matrix, denoted as j. av Since the frame rate of millimeter-wave radar is 20fps, the respiratory rate or heart rate F of human vital signs can be calculated by the following formula:

[0140]

[0141] In this embodiment of the invention, a dynamic signal extraction method is used to suppress interference from static signals, thereby improving the accuracy of human vital sign monitoring in complex environments. In this embodiment of the invention, a diversity approach is used to address the problem of low human echo intensity at long distances by integrating data from multiple antennas and multiple pulses, thus improving the accuracy of human vital sign monitoring at long distances.

[0142] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0143] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, network device, embedded processor, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0145] 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring human vital signs using millimeter-wave radar based on diversity mechanism, characterized in that, The millimeter-wave radar has several transmitting antennas and several receiving antennas; The transmitting antenna is used to transmit millimeter-wave signals to the environment; The receiving antenna is used to receive signals reflected by a human body and objects; the method includes: The signals received by several receiving antennas after being reflected by the human body and objects are mixed. Spatial diversity is performed using the location of the human body to obtain the reflection component of the human body's location; Time diversity is achieved by utilizing the dynamic components of echoes at multiple times to improve the signal-to-noise ratio; Dynamic components are obtained by estimating the static components of the echo and extracting the dynamic signal. The dynamic components are phase extracted and dewound, and bandpass filtered to obtain the vital signs waveform vector. The time domain period estimation is performed on the vital signs waveform vector to obtain the target's respiratory rate and heart rate. The time diversity includes: set up This is the radar data matrix before time diversity processing, where the coordinates are... The elements are ; This is the radar data matrix after time diversity processing, where the coordinates are... The elements are Where M is the number of FMCW pulse frames continuously transmitted by the millimeter-wave radar, K is the number of pulses per frame, N is the number of sampling points for sampling all intermediate frequency signals of the M frames of pulses received by L receiving antennas, and the lowercase letters corresponding to N, K, and M are n, k, and m, which refer to the radar data matrix coordinate elements, that is, the signal data collected at the nth distance sampling point of the kth pulse in the mth frame in the direction of the beam focused on the human body. Based on the number of pulses per frame Calculate the weight vector ; right Using weight vectors We perform a weighted summation to obtain the echo vector after time diversity processing. As shown in the following formula: ； in for The Middle One element; The dynamic signal extraction includes: Enhanced human body position echo Along the first dimension Point FFT is used to obtain the distance spectrum of the echo at the angle of the human body. ; Using the distance between the human body and the radar and distance resolution Calculate the distance unit number where the human body is located. : ； in Indicates rounding down; Will The Middle All elements of a row are denoted as Calculate its center As the static component of the echo at the distance unit of the human body; use The static component of the echo at the distance from the human body Calculate dynamic components ,in The calculation formula is: ； In the formula express The Middle One element, express The Middle Each element.

2. The millimeter-wave radar method for monitoring human vital signs based on diversity mechanism according to claim 1, characterized in that, The spatial diversity includes: set up This is the radar data matrix before spatial diversity processing, where the coordinates are... The elements are ; This is the radar data matrix after spatial diversity processing, where the coordinates are... The elements are Where L is the number of receiving antennas for the millimeter-wave radar, and l represents the l-th receiving antenna; The weight vector is calculated based on the transmitted signal wavelength, antenna array element arrangement, the angle to be measured, and the autocorrelation matrix of the received signal. ; right Using weight vectors Weighted summation is performed to obtain the echo matrix after spatial diversity processing. As shown in the following formula: ； in for The Middle Each element.

3. The millimeter-wave radar method for monitoring human vital signs based on diversity mechanism according to claim 1, characterized in that, The time-domain period estimation method is as follows: Search filtered vital sign waveform vector The maximum value in the vector is recorded, and the index of the element corresponding to the maximum value is stored in the vector. middle, ,in For the first The coordinates corresponding to the maxima for The number of mesolocalities; right Performing first-order difference yields ,in, ; ; calculate The average value of all elements in Human vital signs: respiratory rate or heart rate Calculated by the following formula: ； in, f This refers to the frame frequency of the millimeter-wave radar.

4. A millimeter-wave radar human vital signs monitoring system based on diversity mechanism, characterized in that, The system includes: A millimeter-wave radar having several transmitting antennas and several receiving antennas; the transmitting antennas are used to transmit millimeter-wave signals to the environment; the receiving antennas are used to receive signals reflected by the human body and objects; The signal processing module is used to mix the reflected signal received by the receiving antenna, perform spatial diversity using the location of the human body to obtain the reflected component of the human body location, perform time diversity using the dynamic components of the echo at multiple times to improve the signal-to-noise ratio, extract the dynamic signal by estimating the static component of the echo to obtain the dynamic component, perform phase extraction and dewinding on the dynamic component, perform bandpass filtering, obtain the vital signs waveform vector after filtering, and perform time-domain period estimation on the vital signs waveform vector to obtain the target's respiratory rate and heart rate. The time diversity includes: set up This is the radar data matrix before time diversity processing, where the coordinates are... The elements are ; This is the radar data matrix after time diversity processing, where the coordinates are... The elements are Where M is the number of FMCW pulse frames continuously transmitted by the millimeter-wave radar, K is the number of pulses per frame, N is the number of sampling points for sampling all intermediate frequency signals of the M frames of pulses received by L receiving antennas, and the lowercase letters corresponding to N, K, and M are n, k, and m, which refer to the coordinate elements of the radar data matrix after spatial diversity processing, that is, the signal data collected at the nth distance sampling point of the kth pulse in the mth frame in the direction of the beam focused on the human body. Based on the number of pulses per frame Calculate the weight vector ; right Using weight vectors We perform a weighted summation to obtain the echo vector after time diversity processing. As shown in the following formula: ； in for The Middle One element; The dynamic signal extraction includes: Enhanced human body position echo Along the first dimension Point FFT is used to obtain the distance spectrum of the echo at the angle of the human body. ; Using the distance between the human body and the radar and distance resolution Calculate the distance unit number where the human body is located. : ； in Indicates rounding down; Will The Middle All elements of a row are denoted as Calculate its center As the static component of the echo at the distance unit of the human body; use The static component of the echo at the distance from the human body Calculate dynamic components ,in The calculation formula is: ； In the formula express The Middle One element, express The Middle Each element.

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