A multi-target physiological information extraction method and device based on millimeter wave radar

Abstract

The application discloses a millimeter wave radar-based multi-target physiological information extraction method and device, electronic equipment and storage medium, belonging to the technical field of physiological signal extraction, wherein radar reflection signals of multiple targets are continuously acquired; first reflection signals of different distances are separated from the radar reflection signals; second reflection signals of different angles are separated from the first reflection signals; a spatial filter is established based on the angles and distances corresponding to the second reflection signals; the radar reflection signals are input into the spatial filter to obtain target reflection signals; and physiological information of a target user is acquired based on the target reflection signals. The method can obtain the distances and angles of different targets by sequentially performing distance separation and angle separation on the radar reflection signals superimposed by multiple targets, and the target reflection signals extracted based on the spatial filter established based on the distances and angles can avoid mutual interference of different targets, thereby realizing accurate extraction of physiological information of the target user.

Description

Technical Field

[0001] This application belongs to the field of physiological signal extraction technology, specifically relating to a method, device, electronic device and storage medium for extracting physiological information from multiple targets based on millimeter-wave radar. Background Technology

[0002] The development of artificial intelligence (AI) and the Internet of Things (IoT) technologies has spurred the emergence of intelligent environmental technologies, which hold immense promise in the health sector. Intelligent environmental technologies collect user data through IoT sensors in the environment and utilize AI to assess users' health status; manage medical progress for patients with chronic diseases, adjusting medication promptly; and predict health risks for healthy individuals, promoting timely medical intervention. Basic detection of respiration and heart rate forms the basis of many health assessments; for example, respiratory rate and depth are key indicators for assessing a patient's condition. Other applications, such as those for arrhythmias and sleep apnea, will not be elaborated upon here. Contact-based devices, such as breathing belts and smartwatches, suffer from sensor contact issues. To obtain reliable signals, a certain amount of pressure is required between the sensor and the body part in contact. This can cause user discomfort, leading to reluctance to wear them long-term. Non-contact devices do not have this dependency problem. Furthermore, non-contact devices are more universally applicable to individuals with sensitive skin, such as infants. Infrared cameras are also a possible non-contact method for detecting vital signs, but all user actions and behaviors are recorded, posing a significant threat to user privacy. Radar sensors, while protecting user privacy, extract necessary physiological information. However, radar sensors are subject to complex multipath reflections in the everyday environment, mutual interference between reflected signals from different users, and interference from other moving objects. Extracting reliable multi-person heart rate and respiratory signals remains challenging. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, electronic device and storage medium for extracting physiological information from multiple targets based on millimeter-wave radar to solve the problem of difficulty in distinguishing physiological signals from multiple targets.

[0004] According to a first aspect of the embodiments of this application, a method for extracting physiological information of multiple targets based on millimeter-wave radar is provided, the method including:

[0005] Continuously acquire radar reflection signals from multiple targets;

[0006] Separate the first reflection signals at different distances from the radar reflection signals;

[0007] Separate second reflected signals at different angles from the first reflected signal;

[0008] A spatial filter is established based on the angle and distance corresponding to the second reflected signal;

[0009] The radar reflection signal is input into the spatial filter to obtain the target reflection signal;

[0010] Physiological information of the target user is obtained based on the target reflected signal.

[0011] In some optional embodiments of this application, the radar reflection signals of multiple targets are continuously acquired, including:

[0012] The radar transmits signals to the multiple targets via a linear frequency modulated continuous wave radar.

[0013] The received signal is obtained by receiving the reflected signals superimposed from the multiple targets;

[0014] The difference frequency signal is obtained by mixing the received signal with the transmitted signal;

[0015] The difference frequency signal is input into a low-pass filter to obtain the baseband signal;

[0016] The radar reflection signal is obtained by sampling the baseband signal at preset time intervals.

[0017] In some optional embodiments of this application, the radar transmitted signal is as follows:

[0018]

[0019] Among them, S Tx (t) represents the radar transmitted signal, f c K and B are the start frequency and chirp slope of the FMCW signal, respectively, where K = B / T c B is the chirp bandwidth, T c The duration of the chirp.

[0020] In some optional embodiments of this application, separating first reflection signals at different distances from the radar reflection signals includes:

[0021] The radar reflection signal is converted into a spectral sampling sequence using a fast Fourier transform;

[0022] The peak points in the spectrum sampling sequence are sampled quickly to obtain a snapshot signal;

[0023] Obtain the mean of the spectrum sampling sequence;

[0024] First reflected signals at different distances are separated based on the difference between the captured signal and the mean.

[0025] In some optional embodiments of this application, separating second reflected signals at different angles from the first reflected signal includes:

[0026] A two-dimensional heat map of distance and angle is established based on the first reflected signal;

[0027] The second reflection signal in the two-dimensional heat map is extracted using the constant false alarm rate algorithm.

[0028] In some optional embodiments of this application, before obtaining the physiological information of the target user based on the target reflection signal, the method further includes:

[0029] Obtain the correlation between the target reflection signal and the standard resting state signal;

[0030] The magnitude of the correlation determines whether the target corresponding to the reflected signal is in a resting state.

[0031] In some optional embodiments of this application, after obtaining the physiological information of the target user based on the target reflection signal, the method further includes:

[0032] The estimated location of the target user is predicted using a Kalman filter;

[0033] When the distance between the estimated position and the measured position is greater than a preset value, the spatial filter is updated.

[0034] In some optional embodiments of this application, obtaining the physiological information of the target user based on the target reflection signal includes:

[0035] The respiratory signal is obtained by filtering the target reflection signal using a median filter.

[0036] Peak values ​​are extracted from the respiratory signal to obtain the target user's respiratory sequence.

[0037] In some optional embodiments of this application, obtaining the physiological information of the target user based on the target reflection signal further includes:

[0038] The initial heartbeat signal is obtained by subtracting the respiratory signal from the target reflection signal;

[0039] The initial heartbeat signal is enhanced by a differential filter to obtain a heartbeat signal;

[0040] Peak values ​​are extracted from the heartbeat signal to obtain the target user's heartbeat sequence.

[0041] According to a second aspect of the embodiments of this application, a multi-target physiological information extraction device based on millimeter-wave radar is provided, comprising:

[0042] The first acquisition module is used to continuously acquire radar reflection signals from multiple targets;

[0043] The first separation module is used to separate first reflection signals at different distances from the radar reflection signal;

[0044] The second separation module is used to separate second reflection signals at different angles from the first reflection signal;

[0045] The data processing module is used to establish a spatial filter based on the angle and distance corresponding to the second reflected signal;

[0046] The second acquisition module is used to input the radar reflection signal into the space filter to obtain the target reflection signal;

[0047] The third acquisition module is used to acquire the physiological information of the target user based on the target reflection signal.

[0048] According to a third aspect of the embodiments of this application, an electronic device is provided, which may include:

[0049] processor;

[0050] Memory used to store processor-executable instructions;

[0051] The processor is configured to execute instructions to implement the multi-target physiological information extraction method based on millimeter-wave radar as shown in any embodiment of the first aspect.

[0052] According to a fourth aspect of the embodiments of this application, a storage medium is provided, which, when the instructions in the storage medium are executed by a processor of an information processing device or a server, enables the information processing device or server to implement the multi-target physiological information extraction method based on millimeter-wave radar as shown in any embodiment of the first aspect.

[0053] The above-mentioned technical solution of this application has the following beneficial technical effects:

[0054] The present application provides a method for extracting physiological information of multiple targets based on millimeter-wave radar. By sequentially performing range separation and angle separation on the radar reflection signals of multiple targets superimposed, the distance and angle of different targets can be obtained. The spatial filter established based on the distance and angle can extract the target reflection signals to avoid mutual interference between different targets, thereby achieving accurate extraction of the physiological information of the target user. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a multi-target physiological information extraction method based on millimeter-wave radar according to an exemplary embodiment of this application;

[0056] Figure 2 This is a flowchart illustrating a method for extracting physiological information from multiple targets based on millimeter-wave radar, as described in another exemplary embodiment of this application.

[0057] Figure 3This is a schematic diagram of a multi-target physiological information extraction device based on millimeter-wave radar according to an exemplary embodiment of this application;

[0058] Figure 4 This is a schematic diagram of the electronic device structure in an exemplary embodiment of this application;

[0059] Figure 5 This is a schematic diagram of the hardware structure of an electronic device in an exemplary embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0061] The accompanying drawings illustrate layer structure diagrams according to embodiments of this application. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0062] Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0063] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0064] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0065] Research has found that breathing and heartbeat contain important health information; they reflect the activity levels of the sympathetic and parasympathetic nervous systems; and can be used to monitor sleep apnea, sleep stages, and cardiac arrhythmias. With current technology, when multiple people and other moving objects are present in the radar detection environment, signal interference is severe, making it difficult to accurately extract breathing and heartbeat signals from multiple targets. Therefore, eliminating mutual interference between reflected signals from different individuals is beneficial for improving the signal-to-noise ratio of each individual's signal. This allows for the accurate acquisition of breathing and heart rates from multiple individuals, providing support for other health applications.

[0066] The following description, in conjunction with the accompanying drawings, details the multi-target physiological information extraction method, multi-target physiological information extraction device, electronic device, and storage medium based on millimeter-wave radar provided in this application, through specific embodiments and application scenarios.

[0067] like Figure 1 As shown, in a first aspect of this application, a method for extracting physiological information from multiple targets based on millimeter-wave radar is provided. This method may include:

[0068] Step S101: Continuously acquire radar reflection signals from multiple targets;

[0069] Step S102: Separate the first reflection signals at different distances from the radar reflection signals;

[0070] Step S103: Separate the second reflection signals at different angles from the first reflection signal;

[0071] Step S104: Establish a spatial filter based on the angle and distance corresponding to the second reflected signal;

[0072] Step S105: Input the radar reflection signal into the spatial filter to obtain the target reflection signal;

[0073] Step S106: Obtain the physiological information of the target user based on the target reflection signal.

[0074] In this embodiment, the target is the human body. By sequentially performing range and angle separation on the superimposed radar reflection signals of multiple targets, the distance and angle of different targets can be obtained. A spatial filter based on the range and angle is used to extract the target reflection signals, avoiding interference between different targets and achieving accurate extraction of the target user's physiological information.

[0075] Specifically, step S101 may include: transmitting radar signals to multiple targets via a linear frequency modulated continuous wave radar; receiving reflected signals superimposed from multiple targets to obtain a received signal; mixing the received signal with the transmitted signal to obtain a difference frequency signal; inputting the difference frequency signal into a low-pass filter to obtain a baseband signal; and sampling the baseband signal at a preset time interval to obtain a radar reflection signal.

[0076] In this embodiment, the radar transmits the following signal:

[0077]

[0078] Among them, S Tx (t) represents the radar transmitted signal, j is an imaginary number, and f c K and B are the start frequency and chirp slope of the FMCW signal, respectively, where K = B / T c B is the chirp bandwidth, T c The duration of the chirp.

[0079] In some embodiments, step S102 may include: converting the radar reflection signal into a spectrum sampling sequence by fast Fourier transform; performing fast sampling on the peak points in the spectrum sampling sequence to obtain a fast-sampling signal; obtaining the mean of the spectrum sampling sequence; and separating the first reflection signal at different distances based on the difference between the fast-sampling signal and the mean.

[0080] In some embodiments, step S103 may include: establishing a two-dimensional heat map of distance and angle based on the first reflection signal; and extracting the second reflection signal from the two-dimensional heat map using a constant false alarm rate (CFAR) algorithm.

[0081] like Figure 2 As shown, in a second aspect of this application, a method for extracting physiological information from multiple targets based on millimeter-wave radar is provided. This method includes the following eight steps:

[0082] Step 1: Radar signal transmission and reception

[0083] This scheme selects a linear frequency modulated continuous wave (LFMCW) radar. The radar's transmitted signal S Tx The frequency of (t) increases linearly with the snapshot time t within a chirp:

[0084]

[0085] Where f c and k are the start frequency and chirp slope of the FMCW signal, respectively. Where k = B / T c B is the chirp bandwidth, T cThe duration of the chirp. The signal is reflected by targets within multiple detection areas, and the received signal S... Rx (t) can be considered as the superposition of reflected signals from multiple targets:

[0086]

[0087] Where n is the number of reflective targets; α i R i Let c be the path loss and one-way distance of the i-th target; c is the speed of light. For convenience, let τ be the time delay between the reflected and emitted signals of the i-th target. i =2R i / c. Since the signal frequency increases linearly, the time delay is reflected in the frequency difference between the received and transmitted signals. The difference frequency signal is obtained by mixing the received and transmitted signals, and then a low-pass filter is used to obtain the baseband signal s(t):

[0088]

[0089] Since the signal travels at the speed of light, the second-order term of the time delay... It is a tiny quantity and can be ignored. For reflecting targets at different radial distances, the time delay τ... i The difference is that the frequency kT of the baseband signal s(t) is different. i Different. The baseband signal is sampled at intervals of t. s The sampled signal s(m) is obtained, where m is the sampling number:

[0090]

[0091] Step 2: Distinguish targets by distance

[0092] Reflected targets at different distances will exhibit different frequency components in the mixed signal. The signals from different received channels are then subjected to a Fast Fourier Transform (FFT):

[0093]

[0094] M represents the number of sampling points within a snapshot; S(l) is the complex signal within the first distance. The peak point in the spectral sequence corresponds to one snapshot of the reflective target. By subtracting the mean from a series of snapshots over a period of time, moving reflective targets, such as people, can be identified.

[0095] Step 3: Differentiate targets by angle

[0096] Targets at the same distance are separated through array signal processing. Assume the array has P receiving antennas, with a spacing of d between adjacent antennas. For each angle θ, there is a P×1 dimensional steering vector denoted as A(θ)=[1 e jdsin (θ) / c e j2dsin(θ) / c ...e j(P-1)dsin(θ) / c ] T , where T represents the transpose of the matrix.

[0097] Accumulate Q snapshots over a period of time. For each frequency point m (i.e., at each distance), obtain a P×Q dimensional signal matrix S(m); each element S in the matrix... p，q (m) represents the sample taken by the p-th receiving antenna at distance m at snapshot time q. The correlation matrix is ​​then calculated.

[0098] R s (m)=S(m)S(m) H

[0099] Where H represents the Hamiltonian transpose. The angular spectrum E(m, θ) is obtained using the MVDR algorithm, representing the reflected energy at an angle θ at a distance m in space.

[0100]

[0101] Therefore, a two-dimensional heatmap of distance and angle is obtained. The constant false alarm rate (CFAR) algorithm is used to detect and count the positions of reflecting targets. The angle of arrival and distance of each target are confirmed, and a target table {T1, T2...T...} is established. n}, T i For the i-th reflecting target, the table includes (m i θ i Let E be the coordinate position of the i-th target; and E be the coordinate position of the i-th target. i (m i θ i ) represents the reflected energy value of the i-th target.

[0102] Step 4: Enhance adaptive spatial filter optimization.

[0103] For a single target T at a certain distance l i If you are in a hurry, skip directly to step five.

[0104] Otherwise, based on the energy of multiple targets within a distance l, reconstruct the source signal R of the i-th reflecting target. i (with a dimension of 1×Q) is:

[0105]

[0106] Among them, f iR is the signal frequency of the i-th radiation target; i (q) represents the source signal corresponding to the snapshot signal acquired at time q for the i-th reflected signal, α i Let q be the reflection intensity of the i-th reflected signal, q be the snapshot time, and t be the reflection intensity of the i-th reflected signal. c This refers to the time interval between snapshots.

[0107] The receiver array signal is reconstructed based on the distance and angle of each target. The signal dimension is P×Q:

[0108]

[0109] Where N is P×Q dimensional independent Gaussian noise, and its energy is set to 1% of the signal energy, A(θ) i ) represents the guidance vector for the i-th target angle, 1 represents the current range resolution, and m represents the distance resolution of interest. i The distance to the i-th reflecting target;

[0110] Reconstruct the correlation matrix by reconstructing the received data.

[0111]

[0112] Since the target angle remains unchanged, the angle space of the correlation matrix remains unchanged. Based on the new correlation matrix, the P×1 dimensional filter coefficients h(θ) are then obtained using the MVDR algorithm. i ):

[0113]

[0114] h(θ i ) represents the angle θ corresponding to the i-th reflecting target. i The filter coefficients are applied to the received signal of the receiving array to obtain the i-th target T. i The signal (1×Q) within the Q-time period:

[0115]

[0116] Step Six

[0117] Then take the mean E[T] of the time series. i Estimate the DC bias of the signal:

[0118] DC bias is generally determined by reflections from stationary targets in the environment and direct propagation between transmitting and receiving antennas, which affects the linearity of phase demodulation. Therefore, it is necessary to remove DC information and demodulate the phase using a phase demodulation algorithm. Then, phase compensation is performed at discontinuities. This yields a continuous respiratory and heartbeat waveform φ. i .

[0119] φi =unwrap[angle(T i -E[T i ])]

[0120] in:

[0121]

[0122]

[0123] Next, the signal is correlated with multiple sinusoidal signals of uniform frequency distribution within the breathing range. Based on the magnitude of the correlation, it is determined whether the target is a user in a resting state. A user list is then created by integrating the list of reflecting targets.

[0124] Step Seven: Target Tracking

[0125] The reconstructed signals after separation can be mapped to a specific location. When a user moves, the user needs to be mapped to a new location. At each time refresh point, the location of each target is input into a Kalman filter to predict the location at the next time node. When the next time node arrives, the new measured target location is combined with the predicted target to obtain the estimated location. If the estimated location and the measured location are close, the original filter is used; otherwise, the process returns to step four to update the filter. The new separated signal is then concatenated with the original target signal. This allows for long-term, uninterrupted detection of physiological signals belonging to the same target.

[0126] Step 8: Physiological Information Extraction

[0127] In this step, the respiratory interval and heart rate interval are extracted step by step by processing the mixed phase signal of respiratory and heart rate.

[0128] Before extracting physiological signals, it is necessary to process the raw signal φ of the i-th target. i Some basic processing is done. A 0.02-50Hz bandpass filter is used to remove baseline drift and suppress white noise.

[0129] The steps for extracting the respiratory interval are as follows: The phase signal extracted in the previous step is filtered using a median filter. The filter length should be greater than 2 seconds to extract a smooth respiratory waveform. Since the respiratory waveforms are similar, a peak sequence can be obtained through autocorrelation.

[0130] H b，i (τ)=φ b，i (q-τ)φ b，i (q)

[0131] φ b，i (q) represents the respiratory signal at the i-th target and the q-th time; H b，i(τ) represents the autocorrelation of the signal; τ is the time difference of the autocorrelation.

[0132] Then, the peak position of the respiration can be obtained through the peak detection algorithm, and the respiration sequence can be obtained after differential analysis.

[0133] The steps for extracting heartbeats are as follows: Subtract the respiratory signal extracted using a median filter from the original phase. Then, further amplify the heartbeat signal using a differential filter. The heartbeat peak sequence can be obtained through autocorrelation.

[0134] H h，i (τ)=φ h，i (q-τ)φ h，i (q)

[0135] φ h，i (q) represents the heartbeat signal of the i-th target at the q-th time; H h，i (τ) represents the autocorrelation of the signal; τ is the time difference of the autocorrelation.

[0136] Then, the time nodes of the heart contraction peak can be obtained through the peak detection algorithm, and the heart beat interval sequence can be obtained after differential.

[0137] like Figure 3 As shown, in a third aspect of the embodiments of this application, a multi-target physiological information extraction device based on millimeter-wave radar is provided, comprising:

[0138] The first acquisition module 11 is used to continuously acquire radar reflection signals from multiple targets;

[0139] The first separation module 12 is used to separate first reflection signals at different distances from radar reflection signals;

[0140] The second separation module 13 is used to separate the second reflection signal at different angles from the first reflection signal;

[0141] Data processing module 14 is used to establish a spatial filter based on the angle and distance corresponding to the second reflected signal;

[0142] The second acquisition module 15 is used to input the radar reflection signal into a spatial filter to obtain the target reflection signal;

[0143] The third acquisition module 16 is used to acquire the physiological information of the target user based on the target reflection signal.

[0144] The multi-target physiological information extraction device based on millimeter-wave radar in this application embodiment can also be a component, integrated circuit, or chip in a terminal. This device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0145] The multi-target physiological information extraction device based on millimeter-wave radar provided in this application embodiment can implement each process of the multi-target physiological information extraction method based on millimeter-wave radar provided in any of the above embodiments. To avoid repetition, it will not be described again here.

[0146] Optionally, such as Figure 4 As shown, this application embodiment also provides an electronic device 1100, including a processor 1101, a memory 1102, and a program or instructions stored in the memory 1102 and executable on the processor 1101. When the program or instructions are executed by the processor 1101, they implement the various processes of the above-described embodiment of the multi-target physiological information extraction method based on millimeter-wave radar and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0147] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0148] Figure 5 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0149] The electronic device 1200 includes, but is not limited to, components such as: radio frequency unit 1201, network module 1202, audio output unit 1203, input unit 1204, sensor 1205, display unit 1206, user input unit 1207, interface unit 1208, memory 1209, and processor 1210.

[0150] Those skilled in the art will understand that the electronic device 1200 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1210 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0151] It should be understood that, in this embodiment, the input unit 1204 may include a graphics processing unit (GPU) 12041 and a microphone 12042. The GPU 12041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1206 may include a display panel 12061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 1207 includes a touch panel 12071 and other input devices 12072. The touch panel 12071 is also called a touch screen. The touch panel 12071 may include a touch detection device and a touch controller. Other input devices 12072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here. The memory 1209 can be used to store software programs and various data, including but not limited to applications and operating systems. Processor 1210 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 1210.

[0152] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiments of the multi-target physiological information extraction method based on millimeter-wave radar and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0153] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0154] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the multi-target physiological information extraction method based on millimeter-wave radar, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0155] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0156] It should be noted that, in this document, 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 a process, method, article, or apparatus. Without further limitations, 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. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0157] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0158] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for extracting physiological information from multiple targets based on millimeter-wave radar, characterized in that, include: Continuously acquire radar reflection signals from multiple targets; Separate the first reflection signals at different distances from the radar reflection signals; Separate second reflected signals at different angles from the first reflected signal; A spatial filter is established based on the angle and distance corresponding to the second reflected signal; The radar reflection signal is input into the spatial filter to obtain the target reflection signal; The target reflected signal is subjected to phase demodulation and processing to extract respiratory and heartbeat signals, thereby obtaining the physiological information of the target user; The step of establishing a spatial filter based on the angle and distance corresponding to the second reflected signal includes: Reconstruct the source signal based on the energy of the second reflected signal: ； Among them, f i R is the frequency of the source signal. i (q) represents the source signal corresponding to the snapshot signal acquired at time q for the i-th reflected signal, α i Let be the reflection intensity of the i-th reflected signal. For quick snapshot time, t c This refers to the time interval between snapshots. The receiving array signal is reconstructed based on the distance and angle of the second reflected signal: ； Where N is independent Gaussian noise, To receive array signals, R i For the reconstructed source signal, A(θ) i Let be the steering vector for the i-th target angle, l be the current range resolution, and m be the distance resolution. i The distance to the i-th reflecting target; Reconstruct the correlation matrix based on the reconstructed received array signal; A spatial filter is established based on the reconstructed correlation matrix.

2. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 1, characterized in that, Continuously acquire radar reflection signals from multiple targets, including: The radar transmits signals to the multiple targets via a linear frequency modulated continuous wave radar. The received signal is obtained by receiving the reflected signals superimposed from the multiple targets; The difference frequency signal is obtained by mixing the received signal with the transmitted signal; The difference frequency signal is input into a low-pass filter to obtain the baseband signal; The radar reflection signal is obtained by sampling the baseband signal at preset time intervals.

3. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 2, characterized in that, The radar transmission signal is as follows: ； Among them, S Tx (t) represents the radar transmitted signal, f c K and B are the start frequency and chirp slope of the FMCW signal, respectively, where K = B / T c B is the chirp bandwidth, T c The duration of the chirp.

4. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 1, characterized in that, Separating first reflection signals at different distances from the radar reflection signals includes: The radar reflection signal is converted into a spectral sampling sequence using a fast Fourier transform; The peak points in the spectrum sampling sequence are sampled quickly to obtain a snapshot signal; Obtain the mean of the spectrum sampling sequence; First reflected signals at different distances are separated based on the difference between the captured signal and the mean.

5. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 4, characterized in that, Separating second reflected signals at different angles from the first reflected signal includes: A two-dimensional heat map of distance and angle is established based on the first reflected signal; The second reflection signal in the two-dimensional heat map is extracted using the constant false alarm rate algorithm.

6. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 1, characterized in that, Before obtaining the target user's physiological information based on the target reflected signal, the method further includes: Obtain the correlation between the target reflection signal and the standard resting state signal; The magnitude of the correlation determines whether the target corresponding to the reflected signal is in a resting state.

7. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 1, characterized in that, After obtaining the target user's physiological information based on the target reflected signal, the method further includes: The estimated location of the target user is predicted using a Kalman filter; When the distance between the estimated position and the measured position is greater than a preset value, the spatial filter is updated.

8. The method for extracting multi-target physiological information based on millimeter-wave radar according to claim 1, characterized in that, Phase demodulation and processing are performed on the target reflected signal to extract respiratory and heartbeat signals, thereby obtaining the target user's physiological information, including: The respiratory signal is obtained by filtering the target reflection signal using a median filter. Peak values ​​are extracted from the respiratory signal to obtain the target user's respiratory sequence.

9. A method for extracting physiological information from multiple targets based on millimeter-wave radar according to claim 8, characterized in that, The process of performing phase demodulation and processing on the target reflected signal to extract respiratory and heartbeat signals and obtain physiological information of the target user also includes: The initial heartbeat signal is obtained by subtracting the respiratory signal from the target reflection signal; The initial heartbeat signal is enhanced by a differential filter to obtain a heartbeat signal; Peak values ​​are extracted from the heartbeat signal to obtain the target user's heartbeat sequence.

10. A multi-target physiological information extraction device based on millimeter-wave radar, characterized in that, include: The first acquisition module is used to continuously acquire radar reflection signals from multiple targets; The first separation module is used to separate first reflection signals at different distances from the radar reflection signal; The second separation module is used to separate second reflection signals at different angles from the first reflection signal; The data processing module is used to establish a spatial filter based on the angle and distance corresponding to the second reflected signal. The establishment of the spatial filter includes: Reconstruct the source signal based on the energy of the second reflected signal: ； Among them, f i R is the frequency of the source signal. i (q) represents the source signal corresponding to the snapshot signal acquired at time q for the i-th reflected signal, α i Let be the reflection intensity of the i-th reflected signal. For quick snapshot time, t c This refers to the time interval between snapshots. The receiving array signal is reconstructed based on the distance and angle of the second reflected signal: ； Where N is independent Gaussian noise, To receive array signals, R i For the reconstructed source signal, A(θ) i Let be the steering vector for the i-th target angle, l be the current range resolution, and m be the distance resolution. i The distance to the i-th reflecting target; Reconstruct the correlation matrix based on the reconstructed received array signal; A spatial filter is established based on the reconstructed correlation matrix; The second acquisition module is used to input the radar reflection signal into the space filter to obtain the target reflection signal; The third acquisition module is used to perform phase demodulation and processing on the target reflection signal to extract respiratory and heartbeat signals, thereby obtaining the physiological information of the target user.

11. An electronic device, comprising: include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement a multi-target physiological information extraction method based on millimeter-wave radar as described in any one of claims 1-9.

12. A readable storage medium, characterized by, The readable storage medium stores a program or instructions, which, when executed by a processor, implement a method for extracting multi-target physiological information based on millimeter-wave radar as described in any one of claims 1-9.

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