Wireless signal segmentation method and system for real-time state sensing

Abstract

The invention discloses a wireless signal segmentation method and system for real-time state sensing, and the method is realized based on millimeter wave receiving and transmitting equipment, and comprises the steps: collecting human body target sensing signal data; an MTI algorithm is adopted to remove a multipath effect; signal data at each moment is processed to obtain distance information and incident azimuth angle information, a distance-angle spectrogram matrix representing a target position is constructed, real-time judgment of position attributes is realized, Doppler data is obtained according to a constructed distance-Doppler spectrogram for a walking state, and the position attribute of the target is calculated according to the Doppler data. After segmentation, inputting into a gait recognition network to realize real-time gait recognition; for in-situ activities, Doppler data are obtained according to the constructed distance-Doppler spectrogram; inputting into an action recognition network through dynamic window segmentation to realize real-time action recognition; for in-situ rest, phase information at the Range bin is obtained, the frequency of phase change is obtained, segmentation is conducted through different frequency characteristics of micro-motion interference and respiratory motion, and respiratory monitoring is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless perception and state segmentation, and in particular relates to a wireless signal segmentation method and system for real-time state perception. Background Art

[0002] Wireless sensing technology is the foundation of many intelligent applications and is of great significance for improving people's living standards and promoting the development of social intelligence. For example, in smart homes, wireless sensing technology can sense the user's position and movements in real time, thereby intelligently controlling home appliances such as air conditioners and lighting; in the field of smart medical care, it can monitor the patient's position, movements, vital signs and other information in real time, which is helpful for implementing personalized treatment and health management. At the same time, in the field of rescue and security, this technology can realize the perception of non-contact, non-collaborative movements and vital signs of shielded targets, providing support for emergency rescue and security monitoring. Wireless sensing technology can provide a variety of human target state information for the above applications, including gesture recognition, gait analysis, and vital signs monitoring, which helps to achieve more refined intelligent management and services. It has a wide range of application prospects and can effectively improve the level of intelligence in various fields, bringing a more convenient, safe and efficient life experience.

[0003] In actual application scenarios, real-time perception of the state of human targets is one of the key technologies. To achieve this demand, it is necessary to be able to judge the state changes of human targets in real time, and to perform wireless signal segmentation for effective real-time perception of human target states when the state changes of human targets occur and the conditions for single state perception are met. For example, when the position of the human target moves, gait recognition can be performed; when the human target moves in place, gesture recognition can be performed; when the human target is stationary, vital signs monitoring can be performed. However, most of the existing wireless perception research only perceives a single state, and collects state-related data through constrained perception scenarios for research. For example, in gait recognition research, the subjects are required to maintain a walking state; in gesture recognition research, the subjects are required to move in place and only perform gestures; and in vital signs monitoring research, the subjects are required to remain stationary facing the device. However, in actual daily life, the state of the target changes dynamically and it is difficult to maintain a fixed state. Therefore, how to achieve real-time and accurate perception of multiple human target states in a dynamic environment is still an important challenge facing the current wireless perception field. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the prior art, propose a wireless signal segmentation method for real-time state perception, and also disclose a wireless signal segmentation system for real-time state perception.

[0005] In order to achieve the above object, the present invention proposes a wireless signal segmentation method for real-time state perception, which is implemented based on a millimeter wave transceiver device. The method includes:

[0006] Step 1) collecting human target perception signal data;

[0007] Step 2) Using the MTI algorithm to remove the multipath effect caused by static objects in the environment;

[0008] Step 3) Process the signal data at each moment to obtain distance information and incident azimuth information, respectively, and construct a distance-angle spectrum matrix representing the target position;

[0009] Step 4) Tracking the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attributes of the human target, including walking state, in-situ activity and in-situ stillness;

[0010] For the walking state, a distance-Doppler spectrum is constructed according to the signal data to obtain Doppler data; the Doppler data is segmented with the same duration as the gait recognition samples of the human target, and input into the trained gait recognition network to achieve real-time gait recognition;

[0011] For in-situ activities, a distance-Doppler spectrum is constructed based on signal data to obtain Doppler data; a sliding window is used to detect large-scale motion information, and the Doppler data segmented by a dynamic window is used as the action recognition sample of the human target, which is input into the trained action recognition network to achieve real-time action recognition;

[0012] For stationary state, the phase information at the Range bin is obtained according to the signal data to obtain the frequency of phase change. The different frequency characteristics of micro-motion interference and respiratory movement are used for segmentation to achieve respiratory monitoring.

[0013] Preferably, the millimeter wave transceiver device is a 2-transmit 4-receive antenna millimeter wave transceiver device.

[0014] Preferably, the step 3) comprises:

[0015] Perform FFT processing on the signal frame data S after removing static interference along the fast time axis to obtain the distance information R between the human target and the i-th receiving antenna i :

[0016] R i =RangeFFT(S i ),

[0017] Among them, S i is the data received by the i-th receiving antenna;

[0018] Perform FFT processing along the antenna direction to obtain the incident azimuth information of the target;

[0019] Use the distance information of multiple antennas to perform angle FFT processing and construct the distance-angle spectrum matrix RAM:

[0020] RAM=AngleFFT(R1,...,R i ,...,R I ),

[0021] Wherein, the subscript I is the total number of receiving antennas.

[0022] Preferably, the step 4) tracks the trajectory of the human target based on the distance-angle spectrum matrix, which previously includes: removing environmental noise in the RAM by a constant false alarm rate algorithm.

[0023] Preferably, the step 4) tracks the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attributes of the human target, including walking state, in-situ activity and in-situ stillness; including:

[0024] Through the matrix accumulation method, the distance-angle spectrum matrices of multiple consecutive moments are accumulated. If the position of the human target changes within the set time period, it is judged to be a walking state; if the position of the human target does not change within the set time period and there is human target position information in the distance-angle spectrum, it is judged to be active in situ; if the human position information disappears in the distance-angle spectrum, it is judged to be stationary in situ.

[0025] Preferably, the step 4) constructs a distance-Doppler spectrogram according to the signal data for the walking state to obtain Doppler data; divides the Doppler data into gait recognition samples of the human target with the same duration, and inputs the trained gait recognition network to realize real-time gait recognition; comprising:

[0026] For the walking state, 2D-FFT operation is performed along the fast time axis and slow time axis of the frame data S to construct the range-Doppler spectrum RDM data of the human target;

[0027] For each frame time, the RDM data is summed according to the data in the Range bin where the human target trajectory is located, and the sum is used as the Doppler data of the current frame;

[0028] Segment the Doppler data with the same duration as the gait recognition samples of human targets;

[0029] The trained gait recognition network is input to realize real-time gait recognition; the gait recognition network is CNN.

[0030] Preferably, the step 4) constructs a distance-Doppler spectrum according to the signal data for stationary activities to obtain Doppler data; uses a sliding window to detect large-scale motion information, and uses the Doppler data segmented by a dynamic window as a motion recognition sample for a human target, which is input into a trained motion recognition network to achieve real-time motion recognition; comprising:

[0031] For a human target with a distance of r and an incident azimuth of θ, narrowband filtering is performed in the distance dimension and beamforming is performed in the antenna dimension to obtain focused data.

[0032] Along the data The fast time axis and the slow time axis are subjected to 2D-FFT operation to construct the range-Doppler spectrum RDM data of the human target;

[0033] For each frame time, the RDM data is summed according to the data in the Range bin where the human target trajectory is located, and the sum is used as the Doppler data of the current frame;

[0034] A sliding window is used to detect large-scale motion information. In a sliding window, if the data of the set number of frames has motion information, the current window is judged to be in motion state; and the duration of the dynamic window is automatically increased until the sliding window detects a non-motion state, and the dynamic window representing the motion information ends;

[0035] The Doppler data segmented by dynamic windows are used as action recognition samples of human targets and input into a trained action recognition network to realize real-time action recognition. The action recognition network is a CNN.

[0036] Preferably, the step 4) acquires phase information at the range bin according to the signal data for stationary state, obtains the frequency of phase change, and uses different frequency characteristics of micro-motion interference and respiratory movement for segmentation to realize respiratory monitoring; comprising:

[0037] For stationary state, RangeFFT processing is performed on the average chirp continuous frames along the fast time axis of the frame data S to obtain the Range bin of the micro-scale motion of the human target.

[0038] Extract the phase change of the Range bin and use a bandpass filter to suppress clutter interference;

[0039] The displacement change representing microscale motion is obtained according to the following formula:

[0040]

[0041] Among them, Δφ b is the phase change caused by the rise and fall of the chest cavity when the human body breathes, ΔR is the displacement change of the chest cavity when the human body breathes, and λ is the signal wavelength.

[0042] On the other hand, the present invention provides a wireless signal segmentation system for real-time status perception, which is implemented based on a millimeter wave transceiver device, and the system includes:

[0043] An acquisition module is used to collect human target perception signal data;

[0044] A multipath effect removal module is used to remove the multipath effect caused by static objects in the environment using the MTI algorithm;

[0045] RAM matrix construction module, used to process the signal data at each moment, obtain the distance information and the incident azimuth information respectively, and construct the distance-angle spectrum matrix representing the target position;

[0046] The position attribute judgment module is used to track the trajectory of human targets based on the distance-angle spectrum matrix, and realize the real-time judgment of the position attributes of human targets, including walking status, in-situ activity and in-situ stillness;

[0047] The real-time gait recognition module is used to construct a distance-Doppler spectrum according to the signal data for the walking state to obtain Doppler data; the Doppler data is divided into the same duration as the gait recognition samples of the human target, and input into the trained gait recognition network to realize real-time gait recognition;

[0048] The real-time action recognition module is used to construct a range-Doppler spectrum according to the signal data for activities in place and obtain Doppler data; a sliding window is used to detect large-scale motion information, and the Doppler data segmented by the dynamic window is used as the action recognition sample of the human target, which is input into the trained action recognition network to realize real-time action recognition; the breathing monitoring module is used to obtain the phase information at the Range bin according to the signal data for stationary activities, obtain the frequency of phase change, and use the different frequency characteristics of micro-motion interference and respiratory movement for segmentation to realize breathing monitoring.

[0049] Compared with the prior art, the advantages of the present invention are:

[0050] The present invention overcomes the application limitations of existing wireless state perception research scenario constraints, and realizes real-time segmentation of human target state signals according to position attribute requirements and state characteristic attributes when different states of human targets occur, so that the segmented signals can be used for state perception of human targets; when the target is performing any activity, real-time judgment of the position attributes of the human target can be realized through trajectory tracking, including walking state, in-situ activity, and in-situ stillness; according to the position attribute requirements for perception of different states of human targets, the characteristic attributes of different human target states are utilized to realize real-time segmentation of human target state signals for human target state perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a wireless signal segmentation method for real-time state perception of the present invention;

[0052] Figure 2 It is a schematic diagram of the real-time segmentation principle of gait motion signals;

[0053] Figure 3 It is a schematic diagram of the real-time segmentation principle of large-scale motion signals;

[0054] Figure 4 This is a schematic diagram of the real-time segmentation principle of micro-scale respiratory motion signals. DETAILED DESCRIPTION

[0055] The core idea of ​​the present invention is to achieve real-time segmentation of human target state signals according to the position attribute requirements and state characteristic attributes when different states of the human target occur, so that the segmented signals can be used for state perception of the human target.

[0056] The method flow of the present invention is as follows Figure 1 As shown, the whole is divided into a real-time judgment stage of the human target position attribute and a real-time segmentation stage of the state signal. In the real-time judgment stage of the human target position attribute, the present invention adopts a 2-transmit 4-receive millimeter wave transceiver to collect human target perception signal data. First, the present invention adopts an MTI (Moving Target Inidcation) algorithm to remove the multipath effect caused by static objects in the environment. Then, at each moment, FFT (Fast Fourier Transformation) is performed along the fast time axis direction to obtain the target's Range (distance) information, and FFT is performed along the antenna direction to obtain the target's AoA (Angle of Arrival) information, and RAM information representing the target position is constructed. Furthermore, the environmental noise in RAM (Range-AoA Map) is removed by the CFAR (Constant False-Alarm Rate) algorithm to obtain the position information of the human target. Finally, the RAM matrix at each moment is accumulated to realize the trajectory tracking of the human target by using the matrix accumulation method. According to the trajectory of the human target, real-time judgment of the human target position attribute can be realized. The present invention divides the state of human body moving into walking state, divides the state of human body moving in situ into large-scale motion state, such as gestures, and divides the state of human body stationary into micro-scale motion state, such as chest movement during breathing.

[0057] In the real-time segmentation stage of human target state signals, the present invention realizes real-time segmentation of human target state signals according to the characteristic attributes of different motion states, so that the segmented signals can be used for state perception of human targets. For the walking state, the present invention performs 2DFFT on each frame along the fast time axis and the slow time axis to extract the information of the Doppler change of the target over time. Then, the Doppler data of the same duration are segmented as samples for gait recognition. When large-scale motion and micro-scale motion are segmented in real time, in order to solve the interference of other dynamic targets in the environment, the present invention uses beamforming and narrowband filtering to focus on the active targets in situ. For large-scale motion, 2DFFT is performed on each frame along the fast time axis and the slow time axis to extract the information of the Doppler change of the target over time. Then, the present invention uses a dynamic window mechanism to detect large-scale motion. When large-scale motion is detected in the Doppler data, the detection window begins to increase until the motion disappears, completing the real-time segmentation of the large-scale motion state. For micro-scale motion, the present invention utilizes the characteristic that the phase of the intermediate frequency signal of the FMCW millimeter wave transceiver is sensitive to tiny displacements, extracts the phase information at the range bin where the human target is located, and performs FFT on the phase information to obtain its frequency information. Then, the effective periodic motion in the micro-scale motion is segmented in real time according to the frequency information to remove micro-motion interference, thereby realizing vital sign monitoring. The present invention can realize the real-time segmentation of the perception signals of different motion states of the human target. The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0058] Example 1

[0059] Embodiment 1 of the present invention provides a wireless signal segmentation method for real-time state perception, which is implemented based on a millimeter wave transceiver device. The method includes:

[0060] Step 1) collecting human target perception signal data;

[0061] Step 2) Using the MTI algorithm to remove the multipath effect caused by static objects in the environment;

[0062] Step 3) Process the signal data at each moment to obtain distance information and incident azimuth information, respectively, and construct a distance-angle spectrum matrix representing the target position;

[0063] Step 4) Track the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attribute of the human target. After judging it as a walking state, perform real-time segmentation of gait motion perception signals.

[0064] The system configuration is as follows:

[0065] 1. The system works on the Texas Instrument AWR1443 FMCW millimeter wave transceiver;

[0066] 2. The transceiver operates at 77 GHz and provides a range resolution of 6.7 cm;

[0067] 3. The transceiver device uses 2 transmit and 4 receive antennas, and with the help of TDM-MIMO mode, it can form an 8-element virtual array and provide 15° AoA resolution.

[0068] Task: Based on real-time judgment of human target position attributes, complete real-time segmentation of gait motion perception signals.

[0069] In order to determine the position attribute of the human target in real time, the present invention tracks the trajectory of the human target in real time. The present invention uses a 2-transmit 4-receive millimeter wave transceiver to collect human target perception signal data, and uses the MTI algorithm to remove the multipath effect caused by static objects in the environment. Then, the present invention obtains the position information of the human target in space at the current moment by constructing a RAM graph. Since the distance between the human target and the transceiver is linearly related to the frequency of the intermediate frequency signal, the present invention performs FFT processing on the signal frame data S after removing static interference along the fast time axis, and the distance information between the human target and each receiving antenna can be obtained, which is expressed as:

[0070] R i =RangeFFT(S i ),

[0071] Where S i is the data received by the i-th receiving antenna, R i Indicates the distance between the human target and the i-th receiving antenna. Next, the multi-antenna distance information is used to perform angle FFT, that is, perform FFT along the receiving antenna axis for each range bin to construct RAM, which is expressed as:

[0072] RAM=AngleFFT(R1,...,R i ,...,R I ),

[0073] Where I is the total number of receiving antennas. Furthermore, the present invention removes the environmental noise in the RAM through the CFAR algorithm to obtain the position information of the human target. In order to obtain the trajectory of the human target, the present invention accumulates the RAM matrices of multiple consecutive moments through the matrix accumulation method to realize the trajectory tracking of the human target. According to the trajectory of the human target, the real-time judgment of the position attribute of the human target can be realized. If the trajectory of the human target over a period of time is as follows Figure 2As shown in (a), the position is constantly changing, and it can be judged that the target is in a walking state. In order to obtain the signal of the human target in the walking state for gait recognition, the present invention first performs 2D-FFT operation along the fast time axis and slow time axis of the frame data S to construct the range-Doppler spectrum RDM (Range-Doppler Map) of the human target, which is expressed as:

[0074] D = 2DFFT(S).

[0075] At a frame time, the RDM Figure 2 As shown in (b) in the figure. Then, the data in the range bin where the human target trajectory is located is summed for each frame moment of the RDM, which can be used as the Doppler data of the current frame. The Doppler data is divided into the same duration as the gait recognition sample of the human target, as shown in Figure 2 As shown in (c) in the figure, the sample is sent to the trained gait recognition network to achieve real-time gait recognition.

[0076] Example 2

[0077] Embodiment 1 of the present invention provides a wireless signal segmentation method for real-time state perception, which is implemented based on a millimeter wave transceiver device. The method includes:

[0078] Step 1) collecting human target perception signal data;

[0079] Step 2) Using the MTI algorithm to remove the multipath effect caused by static objects in the environment;

[0080] Step 3) Process the signal data at each moment to obtain distance information and incident azimuth information, respectively, and construct a distance-angle spectrum matrix representing the target position;

[0081] Step 4) Track the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attribute of the human target. After judging that it is an in-situ activity, real-time segmentation of the motion perception signal is performed.

[0082] The system configuration is as follows:

[0083] 1. The system works on the Texas Instrument AWR1443 FMCW millimeter wave transceiver;

[0084] 2. The transceiver operates at 77 GHz and provides a range resolution of 6.7 cm;

[0085] 3. The transceiver device uses 2 transmit and 4 receive antennas, and with the help of TDM-MIMO mode, it can form an 8-element virtual array and provide 15° AoA resolution.

[0086] Task: Based on real-time judgment of human target position attributes, complete real-time segmentation of motion perception signals.

[0087] After determining that the human target is in a state of in-situ activity through trajectory tracking, the present invention performs large-scale motion signal segmentation for action recognition. In the real-time judgment stage of position attributes, the present invention realizes the judgment of the target position attribute based on RAM trajectory tracking. After determining that the human target is in a state of large-scale motion, the action signal segmentation is performed for action recognition, such as Figure 3 Specifically, for the signal frame data S after removing static interference, FFT is performed along the fast time axis direction and the antenna axis direction to construct RAM, and the position information of the human target can be extracted. Figure 3 As shown in (a), when the trajectory of a human target remains unchanged over a period of time, it can be judged that the target is in a large-scale motion state. The present invention can suppress the interference of targets in other directions by focusing on the human target. For a human target with a distance of r and an AoA of θ, narrowband filtering is performed in the distance dimension and beamforming is performed in the antenna dimension to obtain focused data. In order to obtain the motion signal of the human target, the present invention firstly The fast time axis and slow time axis are subjected to 2D-FFT operation to construct RDM. The RDM of a frame is as follows Figure 3 As shown in (b) in the figure. Then, the data within the Range bin where the human target is located is summed for the RDM at each frame moment, which can be used as the Doppler data of the current frame. The present invention designs a dynamic window mechanism and uses a sliding window to detect large-scale motion information. In a sliding window, if the data of 4 / 5 frames have action information, the present invention determines that the current window is in an action state. When the sliding window detects an action state, the duration of the dynamic window automatically increases until the sliding window detects a non-action state, and the dynamic window representing the action information ends, as shown in FIG. Figure 3 As shown in (c) in the figure, the Doppler data segmented by the dynamic window is used as the action recognition sample of the human target and sent to the trained action recognition network to realize real-time action recognition.

[0088] Example 3

[0089] Embodiment 1 of the present invention provides a wireless signal segmentation method for real-time state perception, which is implemented based on a millimeter wave transceiver device. The method includes:

[0090] Step 1) collecting human target perception signal data;

[0091] Step 2) Using the MTI algorithm to remove the multipath effect caused by static objects in the environment;

[0092] Step 3) Process the signal data at each moment to obtain distance information and incident azimuth information, respectively, and construct a distance-angle spectrum matrix representing the target position;

[0093] Step 4) Track the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attribute of the human target. After judging that the human target is stationary, real-time segmentation of the breathing perception signal is performed.

[0094] The system configuration is as follows:

[0095] 1. The system works on the Texas Instrument AWR1443 FMCW millimeter wave transceiver;

[0096] 2. The transceiver operates at 77 GHz and provides a range resolution of 6.7 cm;

[0097] 3. The transceiver device uses 2 transmit and 4 receive antennas, and with the help of TDM-MIMO mode, it can form an 8-element virtual array and provide 15° AoA resolution.

[0098] Task: Based on the real-time judgment of the human target position attributes, complete the real-time segmentation of the respiratory perception signal.

[0099] After the human target is determined to be in a stationary state through trajectory tracking, the present invention performs micro-scale motion signal segmentation for real-time respiratory monitoring, such as Figure 4 In the real-time judgment stage of the human target position attribute, if the target position disappears in the RAM, it can be judged that the current target is in a micro-scale motion state, such as Figure 4 As shown in (a) in the state signal real-time segmentation stage, since the phase information of the intermediate frequency signal of the FMCW millimeter wave transceiver is sensitive to small displacements, the present invention first extracts the phase information of the range bin where the human target is located. Then, the present invention determines whether there is micro-motion interference based on the frequency information of the phase change, so as to segment the effective periodic motion signal in the micro-scale motion in real time to realize respiratory monitoring.

[0100] In order to obtain the range bin where the target is located, the present invention improves the frequency resolution and signal-to-noise ratio by combining multi-frame analysis. Specifically, the chirp contained in a frame is defined as:

[0101] chirp i =r(t)+n i (t),i=1,2,...,N,

[0102] Among them, chirp i Is a frame th chirps, N is the total number of chirps in a frame, r(t) is the intermediate frequency signal, ni (t) is the Gaussian noise in the chirp. In a frame, r(t) is considered to be fixed, and the average value of all chirps in a frame is calculated along each range bin to represent the average chirp of each frame, which is defined as:

[0103] AC(t)=r(t)+n(t).

[0104] Then, the average chirps of different frames are combined along time to form an observation time window. An observation time window can be defined as:

[0105] window = {AC k (t)|k=1,2,...,F},

[0106] Among them, AC k k th The average chirp of the frame in the time window, F is the number of frames in the time window. By performing RangeFFT on the average chirp continuous frames along the fast time axis of each frame, the rangebin of the target micro-scale motion can be obtained, such as Figure 4 By extracting the phase change of the range bin, the displacement change of the microscale motion can be expressed as

[0107]

[0108] Among them, Δφ b is the phase change caused by the chest fluctuation when the human body breathes, ΔR is the displacement of the chest when the human body breathes, and λ is the signal wavelength. Since the extracted original phase change contains obvious clutter interference, a bandpass filter is used to suppress the clutter interference and obtain the phase change caused by micro-scale motion, such as Figure 4 In order to distinguish the micro-scale chest motion caused by breathing from micro-motion interference, the frequency of phase change is obtained by performing FFT on each observation time window. When there are multiple frequency components, it is considered that there is micro-motion interference, such as Figure 4 When the frequency component comes only from the micro-scale motion of the chest caused by breathing, it is considered to be micro-scale respiratory motion, such as Figure 4 As shown in (f) in the figure, the phase signal caused by segmentation of breathing can be used to realize breathing monitoring.

[0109] Example 4

[0110] Embodiment 4 of the present invention provides a wireless signal segmentation system for real-time state perception, which is implemented based on a millimeter wave transceiver device. The system is implemented using the methods of Embodiments 1-3, including:

[0111] An acquisition module is used to collect human target perception signal data;

[0112] A multipath effect removal module is used to remove the multipath effect caused by static objects in the environment using the MTI algorithm;

[0113] RAM matrix construction module, used to process the signal data at each moment, obtain the distance information and the incident azimuth information respectively, and construct the distance-angle spectrum matrix representing the target position;

[0114] The position attribute judgment module is used to track the trajectory of human targets based on the distance-angle spectrum matrix, and realize the real-time judgment of the position attributes of human targets, including walking status, in-situ activity and in-situ stillness;

[0115] The real-time gait recognition module is used to construct a distance-Doppler spectrum according to the signal data for the walking state to obtain Doppler data; the Doppler data is divided into the same duration as the gait recognition samples of the human target, and input into the trained gait recognition network to realize real-time gait recognition;

[0116] The real-time action recognition module is used to construct a distance-Doppler spectrum according to the signal data for in-situ activities to obtain Doppler data; a sliding window is used to detect large-scale motion information, and the Doppler data segmented by the dynamic window is used as the action recognition sample of the human target, which is input into the trained action recognition network to realize real-time action recognition;

[0117] The respiratory monitoring module is used to obtain the phase information at the Range bin according to the signal data when the patient is stationary, obtain the frequency of the phase change, and use the different frequency characteristics of micro-motion interference and respiratory movement to segment and realize respiratory monitoring.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. A wireless signal segmentation method for real-time status perception, implemented based on a millimeter wave transceiver device, the method comprising: Step 1) collecting human target perception signal data; Step 2) Using the MTI algorithm to remove the multipath effect caused by static objects in the environment; Step 3) Process the signal data at each moment to obtain distance information and incident azimuth information, respectively, and construct a distance-angle spectrum matrix representing the target position; Step 4) Tracking the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attributes of the human target, including walking state, in-situ activity and in-situ stillness; For the walking state, a distance-Doppler spectrum is constructed according to the signal data to obtain Doppler data; the Doppler data is segmented with the same duration as the gait recognition samples of the human target, and input into the trained gait recognition network to achieve real-time gait recognition; For in-situ activities, a distance-Doppler spectrum is constructed based on signal data to obtain Doppler data; a sliding window is used to detect large-scale motion information, and the Doppler data segmented by a dynamic window is used as the action recognition sample of the human target, which is input into the trained action recognition network to achieve real-time action recognition; For stationary state, the phase information at the Range bin is obtained according to the signal data to obtain the frequency of phase change. The different frequency characteristics of micro-motion interference and respiratory movement are used for segmentation to achieve respiratory monitoring.

2. The wireless signal segmentation method for real-time status perception according to claim 1, characterized in that: The millimeter wave transceiver device is a 2-transmit 4-receive antenna millimeter wave transceiver device.

3. The wireless signal segmentation method for real-time status perception according to claim 2, characterized in that: The step 3) comprises: Perform FFT processing on the signal frame data S after removing static interference along the fast time axis to obtain the distance information R between the human target and the i-th receiving antenna i : R i =RangeFFT(S i ), Among them, S i is the data received by the i-th receiving antenna; RangeFFT() represents the distance between the human target and the i-th receiving antenna; Perform FFT processing along the antenna direction to obtain the incident azimuth information of the target; Use the distance information of multiple antennas to perform angle FFT processing and construct the distance-angle spectrum matrix RAM: RAM=AngleFFT(R1,...,R i ,...,R I ), Wherein, the subscript I is the total number of receiving antennas, and AngleFFT(·) represents the angle information between the human target and the i-th receiving antenna.

4. The wireless signal segmentation method for real-time status perception according to claim 3, characterized in that: The step 4) tracks the trajectory of the human target based on the distance-angle spectrum matrix, which previously includes: removing environmental noise in the RAM by a constant false alarm rate algorithm.

5. The wireless signal segmentation method for real-time status perception according to claim 3, characterized in that: The step 4) tracks the trajectory of the human target based on the distance-angle spectrum matrix to achieve real-time judgment of the position attributes of the human target, including walking state, in-situ activity and in-situ stillness; including: The distance-angle spectrum matrices of multiple consecutive moments are accumulated through the matrix accumulation method. If the position of the human target changes within the set time period, it is judged to be a walking state; if the position of the human target does not change within the set time period and there is human target position information in the distance-angle spectrum, it is judged to be active in situ; if the human position information disappears in the distance-angle spectrum, it is judged to be stationary in situ.

6. The wireless signal segmentation method for real-time status perception according to claim 3, characterized in that: In step 4), for the walking state, a distance-Doppler spectrum is constructed according to the signal data to obtain Doppler data; Doppler data with the same duration segmentation is used as gait recognition samples of human targets, and input into the trained gait recognition network to achieve real-time gait recognition; including: For the walking state, 2D-FFT operation is performed along the fast time axis and slow time axis of the frame data S to construct the range-Doppler spectrum RDM data of the human target; For each frame time, the RDM data is summed according to the data in the Range bin where the human target trajectory is located, and the sum is used as the Doppler data of the current frame; Segment the Doppler data with the same duration as the gait recognition samples of human targets; The trained gait recognition network is input to realize real-time gait recognition; the gait recognition network is CNN.

7. The wireless signal segmentation method for real-time status perception according to claim 3, characterized in that: The step 4) constructs a distance-Doppler spectrum according to the signal data for the in-situ activity to obtain Doppler data; uses a sliding window to detect large-scale motion information, uses the Doppler data segmented by a dynamic window as a motion recognition sample for a human target, and inputs the trained motion recognition network to realize real-time motion recognition; including: For a human target with a distance of r and an incident azimuth of θ, narrowband filtering is performed in the distance dimension and beamforming is performed in the antenna dimension to obtain focused data. Along the data The fast time axis and the slow time axis are subjected to 2D-FFT operation to construct the range-Doppler spectrum RDM data of the human target; For each frame time, the RDM data is summed according to the data in the Range bin where the human target trajectory is located, and the sum is used as the Doppler data of the current frame; A sliding window is used to detect large-scale motion information. In a sliding window, if the data of the set number of frames has motion information, the current window is judged to be in motion state; and the duration of the dynamic window is automatically increased until the sliding window detects a non-motion state, and the dynamic window representing the motion information ends; The Doppler data segmented by dynamic windows are used as action recognition samples of human targets and input into a trained action recognition network to realize real-time action recognition. The action recognition network is a CNN.

8. The wireless signal segmentation method for real-time status perception according to claim 3, characterized in that: The step 4) for stationary state, obtaining phase information at the range bin according to the signal data, obtaining the frequency of phase change, and segmenting using different frequency characteristics of micro-motion interference and respiratory movement to achieve respiratory monitoring; includes: For stationary state, RangeFFT processing is performed on the average chirp continuous frames along the fast time axis of the frame data S to obtain the Range bin of the micro-scale motion of the human target. Extract the phase change of the Range bin and use a bandpass filter to suppress clutter interference; The displacement change representing microscale motion is obtained according to the following formula: Among them, Δφ b is the phase change caused by the rise and fall of the chest cavity when the human body breathes, ΔR is the displacement change of the chest cavity when the human body breathes, and λ is the signal wavelength.

9. A wireless signal segmentation system for real-time status perception, based on a millimeter wave transceiver, characterized in that: The system comprises: An acquisition module is used to collect human target perception signal data; A multipath effect removal module is used to remove the multipath effect caused by static objects in the environment using the MTI algorithm; RAM matrix construction module, used to process the signal data at each moment, obtain the distance information and the incident azimuth information respectively, and construct the distance-angle spectrum matrix representing the target position; The position attribute judgment module is used to track the trajectory of human targets based on the distance-angle spectrum matrix, and realize the real-time judgment of the position attributes of human targets, including walking status, in-situ activity and in-situ stillness; A real-time gait recognition module is used to construct a distance-Doppler spectrogram according to the signal data for the walking state to obtain Doppler data; the Doppler data is segmented with the same duration as the gait recognition samples of the human target, and input into the trained gait recognition network to realize real-time gait recognition; and The real-time action recognition module is used to construct a distance-Doppler spectrum according to the signal data for stationary activities to obtain Doppler data; a sliding window is used to detect large-scale motion information, and the Doppler data segmented by the dynamic window is used as the action recognition sample of the human target, which is input into the trained action recognition network to realize real-time action recognition; and The respiratory monitoring module is used to obtain the phase information at the Range bin according to the signal data when the patient is stationary, obtain the frequency of the phase change, and use the different frequency characteristics of micro-motion interference and respiratory movement to segment and realize respiratory monitoring.

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