Radar-based vital sign detection method, device, and storage medium

Abstract

The application discloses a kind of based on radar vital sign detection method, equipment and storage medium, the detection method includes target detection to radar echo, obtain real target and its distance gate and respiratory rate at;The phase signal of the distance gate where real target is and the phase signal of the adjacent distance gate of the distance gate where real target is are normalized superimposed and unwound, and the vital sign signal of real target is obtained;Heartbeat band of vital sign signal is Chrip-Z development and maximum peak frequency search is carried out, and target heart rate is obtained.The phase signal of multiple distance gates is normalized fusion in the application, the signal-to-noise ratio of respiration and heartbeat signal is improved, and vital sign information carried by radar echo is fully utilized;High-resolution spectrum analysis in heartbeat band is realized by Chrip-Z development, heartbeat signal spectrum is refined, and the detection precision of vital sign parameter is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of radar, and particularly relates to a radar-based vital sign detection method, device and storage medium. BACKGROUND

[0002] The existing radar vital sign detection method has the following technical problems:

[0003] (1) The detection and positioning of the target are usually realized by a single technical means of constant false alarm rate (CFAR) detection or distance FFT slow-time signal variance (or energy) after clutter suppression. However, in a complex environment, the clutter suppression cannot completely cancel the static clutter, so that the above detection method is prone to false alarm, affecting the performance of the radar.

[0004] (2) The vital sign signal of the target is realized by extracting the phase signal on the strongest distance bin of the target, but the echo of the human chest covers multiple distance bins, and each distance bin contains the vital sign signal of the target, so the radar echo signal is not fully utilized.

[0005] (3) The conventional Fourier transform (FFT) is suitable for analyzing the spectrum of the entire sampling signal, but lacks refinement in a certain frequency range, resulting in poor detection accuracy of the vital sign parameters of the target. In the radar vital sign detection, only the frequency range of human respiration and heartbeat (for example, 0.1~0.6Hz, 1.0~2.0Hz) is usually concerned. SUMMARY

[0006] The present application aims to provide a radar-based vital sign detection method, device and storage medium, to solve at least one of the problems that the conventional technology analyzes the entire sampling spectrum, resulting in low detection accuracy of the vital sign parameters of the target, the clutter suppression cannot completely cancel the static clutter, resulting in false alarm, and the radar echo signal is not fully utilized.

[0007] The present application solves the above technical problems by the following technical solution: a radar-based vital sign detection method, comprising:

[0008] acquiring a radar echo;

[0009] performing target detection on the radar echo to obtain a real target and a distance gate and a respiration frequency where the real target is located;

[0010] normalizing and superimposing the phase signal of the distance gate where the real target is located and the phase signal of the adjacent distance gate of the distance gate where the real target is located, and unwrapping to obtain a vital sign signal of the real target;

[0011] Chrip-Z expansion is performed on the heartbeat band of the vital sign signal, and a maximum peak frequency search is performed to obtain a target heart rate.

[0012] Further, target detection is performed on the radar echo, including:

[0013] Fast Fourier transform is performed on the slow-time signal of the radar echo to obtain a signal spectrum;

[0014] Amplitude normalization and threshold-based peak frequency search in the respiratory band are performed on the signal spectrum to obtain a potential target and a distance gate and a respiratory frequency where the potential target is located;

[0015] Clutter suppression and two-dimensional fast Fourier transform are performed on the radar echo to obtain an echo range-Doppler spectrum;

[0016] Zero-channel digital beamforming imaging is performed on the echo range-Doppler spectrum to obtain an echo range-angle spectrum;

[0017] CFAR detection is performed on the echo range-angle spectrum;

[0018] According to the potential target and the distance gate and the respiratory frequency where the potential target is located, and the CFAR detection result, a real target and a distance gate and a respiratory frequency where the real target is located are obtained.

[0019] Further, before the fast Fourier transform is performed on the slow-time signal of the radar echo, the detection method further includes:

[0020] Low-pass filtering is performed on the slow-time signal of the radar echo;

[0021] Self-convolution is performed on the low-pass filtered slow-time signal to obtain a self-convolved slow-time signal.

[0022] Further, the clutter suppression is performed on the radar echo by using a sliding window averaging cancellation method, and a specific formula is as follows:

[0023] ;

[0024] ;

[0025] wherein, denotes the rth frame of radar echo after clutter suppression; denotes the rth frame of radar echo; denotes a background data estimation value; Q denotes the number of accumulated frames; denotes the r-qth frame of radar echo.

[0026] Further, the zero-channel digital beamforming imaging is performed on the echo range-Doppler spectrum, including:

[0027] extract Doppler zero velocity channel data from the echo range-Doppler spectrum;

[0028] multiplying the multi-channel Doppler zero velocity channel data as the input of digital beamforming imaging with a steering vector array to obtain an echo range-angle spectrum; wherein the steering vector array is composed of steering vectors of different antenna array elements and different angle beams, and a generation formula of the steering vector is:

[0029] ;

[0030] wherein, denotes a steering vector; denotes an azimuth coordinate of an antenna array element; denotes an azimuth angle of a beam direction; , denotes a radar wavelength.

[0031] Further, a specific calculation formula of the threshold value of the CFAR detection is:

[0032] ;

[0033] ;

[0034] wherein, T denotes a threshold value; denotes a product factor; G denotes a reference unit quantity; denotes an i-th reference unit value; denotes an expected false alarm probability.

[0035] Further, the heartbeat frequency band of the vital sign signal is Chrip-Z expanded, including:

[0036] determining a spiral sampling parameter according to the heartbeat frequency band;

[0037] substituting the spiral sampling parameter into a Z transform of the vital sign signal to realize Chrip-Z expansion.

[0038] Based on the same concept, the present application also provides an electronic device, including a memory, a processor and a computer program / instruction stored on the memory, wherein the processor executes the computer program / instruction to realize the vital sign detection method as described above.

[0039] Based on the same concept, the present application also provides a computer readable storage medium, which stores a computer program / instruction, and the computer program / instruction is executed by a processor to realize the vital sign detection method as described above.

[0040] Compared with the prior art, the present application has the beneficial effects that:

[0041] The application carries out normalized fusion on the phase signals of the multi-distance gate, improves the signal-to-noise ratio of the respiration and heartbeat signals, fully utilizes the vital sign information carried by the radar echo, and is beneficial to improving the detection accuracy of the vital sign parameters; the high-resolution spectrum analysis in the heartbeat frequency band is realized through Chrip-Z expansion, the heartbeat signal spectrum is refined, and the detection accuracy of the vital sign parameters is improved. The slow time domain target detection and the CFAR target detection are combined to perform composite judgment, and false alarm is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the following embodiment description will be briefly introduced. Obviously, the drawings in the following description are only one embodiment of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 is a flow chart of the vital sign detection method in the embodiment of the present application;

[0044] Figure 2 is a signal amplitude comparison chart before and after self-convolution in the embodiment of the present application;

[0045] Figure 3 is a signal amplitude comparison chart of normalized superposition of single distance gate and multi-distance gate in the embodiment of the present application;

[0046] Figure 4 is a Chrip-Z expansion schematic diagram of the heartbeat frequency band in the embodiment of the present application;

[0047] Figure 5 is a maximum peak frequency search schematic diagram in the heartbeat frequency band in the embodiment of the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the present application will be described clearly and completely in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the present application.

[0049] The technical solutions of the present application will be described in detail in the following specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0050] Embodiment one

[0051] As Figure 1As shown, the radar-based vital sign detection method provided by the embodiment of the application comprises the following steps:

[0052] Step 1: Obtain radar echo.

[0053] Step 2: Perform target detection on the slow-time signal of the radar echo to obtain potential targets and distance gates and respiratory frequencies at which the potential targets are located;

[0054] Step 3: Perform target detection on the radar echo to obtain a CFAR detection result;

[0055] Step 4: Obtain real targets and distance gates and respiratory frequencies at which the real targets are located according to the potential targets and the distance gates at which the potential targets are located and the CFAR detection result;

[0056] Step 5: Perform normalized superposition and unwrapping on the phase signal of the distance gate at which the real target is located and the phase signal of the adjacent distance gate of the distance gate at which the real target is located to obtain a vital sign signal of the real target;

[0057] Step 6: Perform Chrip-Z expansion on the heartbeat frequency band of the vital sign signal and perform maximum peak frequency search to obtain a target heart rate.

[0058] In the specific embodiment of the application, in Step 2, the target detection on the slow-time signal of the radar echo comprises:

[0059] Step 2.1: Perform 2Hz low-pass filtering on the slow-time signal of the radar echo to filter out high-frequency signal interference; wherein the slow-time signal refers to a sequence of the echo signal received by a same distance gate in different pulse transmission periods.

[0060] Step 2.2: Perform self-convolution on the slow-time signal after low-pass filtering to obtain a slow-time signal after self-convolution.

[0061] The correlation of the signal after self-shifting is used to enhance the energy of the periodic component in the signal, effectively suppresses irrelevant random noise, and improves the signal-to-noise ratio. In this embodiment, the self-convolution formula is:

[0062] , (1)

[0063] wherein, represents the slow-time signal after self-convolution; represents the slow-time signal after low-pass filtering; N represents the length of the slow-time signal; n and m each represent a sampling point serial number.

[0064] Figure 2 FIG. 2 shows a comparison of amplitudes before and after self-convolution of the signal, from which it can be seen that the energy of the periodic component in the signal is enhanced after self-convolution. Figure 2It can be seen that after the signal self-convolution, the signal-to-noise ratio of the respiratory frequency 0.35 Hz and the heart rate 1.5 Hz is increased by about 20 dB and 8 dB respectively compared with before.

[0065] Step 2.3: Fast Fourier transform is performed on the slow-time signal after self-convolution to obtain a signal spectrum.

[0066] Step 2.4: Amplitude normalization and threshold-based peak frequency search in the respiratory frequency band are performed on the signal spectrum to obtain a potential target and a distance gate and a respiratory frequency where the potential target is located.

[0067] The normal human respiratory frequency band is 0.1-0.6 Hz, so the threshold-based peak frequency search is performed in the range of 0.1-0.6 Hz. If a peak frequency with an amplitude exceeding an amplitude threshold is detected, the distance gate has a potential target, and there can be multiple peak frequencies exceeding the amplitude threshold, wherein the maximum peak frequency is the respiratory frequency of the potential target; if no peak frequency with an amplitude exceeding the amplitude threshold is detected, the distance gate has no potential target, and the slow-time signal detection of the next distance gate is performed. In this embodiment, the amplitude threshold is set to 0.8.

[0068] In the specific embodiments of the present application, in step 3, the target detection on the radar echo includes:

[0069] Step 3.1: Clutter suppression is performed on the radar echo.

[0070] In this embodiment, the sliding window averaging cancellation method is used to suppress the clutter of the radar echo, and the specific formula is:

[0071] (2)

[0072] (3)

[0073] wherein, represents the rth frame of radar echo after clutter suppression; represents the rth frame of radar echo; represents a background data estimation value; Q represents the number of accumulated frames (i.e. the number of received frames); represents the r-qth frame of radar echo.

[0074] Step 3.2: Two-dimensional fast Fourier transform is performed on the radar echo after clutter suppression to obtain a range-Doppler spectrum.

[0075] The fast Fourier transform is performed on the radar echo after clutter suppression along the range dimension and the velocity dimension respectively, and the range-Doppler spectrum is obtained.

[0076] The radar has multiple antenna elements, each of which has a fast time-slow time radar echo, a Doppler dimension, and each Doppler pulse represents a different speed channel.

[0077] Step 3.3: Zero-channel digital beamforming imaging is performed on the echo range-Doppler spectrum to obtain an echo range-angle spectrum.

[0078] Digital beamforming imaging (DBF imaging) mainly focuses on the azimuth of the target by calculating the time delay of the target to each equivalent element, thereby extracting the azimuth information. Static targets are concentrated in the Doppler zero-speed channel, so the Doppler zero-speed channel data (i.e., the pulse data of zero speed) is extracted from the echo range-Doppler spectrum, and then the multi-channel Doppler zero-speed channel data is taken as the input of DBF imaging and multiplied by the steering vector array to obtain an echo range-angle spectrum. The steering vector array is composed of steering vectors of different antenna elements and different angle beams, and the generation formula of the steering vector is:

[0079] (4)

[0080] wherein, represents the steering vector; represents the azimuth coordinate of the antenna element; represents the azimuth angle of the beam pointing; , represents the radar wavelength.

[0081] Suppose the number of antenna elements is 8, the beam angle to be pointed is-30°~30°, and there is a beam every 1 degree, then the number of angle beams is 61 (including 0°), and an 8x61 steering vector array can be generated by formula (4).

[0082] Step 3.4: CFAR detection is performed on the echo range-angle spectrum.

[0083] In the specific embodiment of the present application, the specific calculation formula of the threshold value of the CFAR detection is:

[0084] (5)

[0085] (6)

[0086] wherein, T represents the threshold value; represents the product factor; represents the reference cell average value; G represents the reference cell number; represents the i-th reference cell value (i.e., the value of the selected sampling point in the radar echo two-dimensional matrix); represents the expected false alarm probability.

[0087] If the value of the detection unit is greater than the threshold value T, it is considered that the detection unit has the target. In this embodiment, the false alarm probability is set to 1e -6 According to the low resolution and high real-time requirement of biological radar, the reference unit number is set to 16.

[0088] In a complex scene, the clutter suppression effect is not good, which leads to false alarm in CFAR detection. In order to reduce false alarm, in step 4, the potential target and the distance gate where it is located are combined with the CFAR detection result to determine whether there is a real target in the distance gate. When there is a potential target (i.e. the target detected in step 2) and a CFAR detection target (i.e. the target detected in step 3) in the same distance gate, the distance gate has a real target, and the breathing frequency of the potential target is the breathing frequency of the real target.

[0089] The target vital sign frequency (the strongest amplitude of the respiratory signal frequency) is carried in the slow-time signal spectrum, and the static clutter frequency after clutter suppression is not completely cancelled, which is direct current. Based on this feature, combined with CFAR detection, false alarm can be reduced.

[0090] The target breathing and heartbeat signals have periodicity and stable signal strength, while the noise is random. In step 5, the phase signal of the distance gate where the real target is located is normalized and superimposed with the phase signal of the adjacent distance gate where the real target is located, and the signal-to-noise ratio of the weak heartbeat signal in the vital sign signal is improved through multiple distance gates, and the vital sign information carried by the radar echo is fully utilized. Figure 3 The normalized superimposed signal amplitude comparison chart of a single distance gate and multiple distance gates is shown in Figure 3 It can be seen that after normalization and superposition, the signal-to-noise ratio of the heart rate 1.3Hz is improved by about 4dB compared with before normalization and superposition.

[0091] In the specific embodiment of the present application, in step 6, Chrip-Z expansion of the heartbeat band of the vital sign signal includes:

[0092] Step 6.1: Determine the spiral sampling parameters according to the heartbeat band.

[0093] The spiral sampling parameters are:

[0094] (7)

[0095] Wherein, represents taking k sampling points on the Z transform plane; represents the radius length of the Z transform plane where the sampling point is located; represents the starting phase angle; represents the extension rate of the spiral line, greater than 1 means inward shrinking, less than 1 means outward extension; represents the equal angle between sampling points on the spiral line, which is determined by (spread end frequency-starting frequency) / (signal sampling rate*refinement point number). , , represents the refinement point number.

[0096] In this embodiment, the Z transform plane is a unit circle, and the heart beat band is 1.0~2.0Hz, thus: , , , , represents the sampling frequency of the slow time signal, and M is set as 500.

[0097] Step 6.2: the spiral sampling parameters are substituted into the Z transform of the vital sign signal to realize Chrip-Z spread of the heart beat band:

[0098] (8)

[0099] wherein, represents the Z transform of the vital sign signal; represents the vital sign signal; represents the length of the vital sign signal. , , , as shown in Figure 4 , the Chrip-Z spread can be represented by convolution as:

[0100] (9)

[0101] The maximum peak frequency search is performed on the heart beat band of the vital sign signal, and the maximum peak frequency is the target heart rate. As shown in Figure 5 , the maximum peak frequency is 1.53Hz, and the target heart rate is 1.530Hz, and the accuracy reaches 0.001Hz.

[0102] The Chrip-Z transform calculates the Z transform sampling points of any frequency interval through convolution and multiplication, realizes high-resolution spectrum analysis of any frequency range, refines the heart beat signal band, and can more finely extract the target vital sign parameter.

[0103] Embodiment two

[0104] The embodiment of the application further provides an electronic device, which comprises a memory, a processor and a computer program / instruction stored in the memory, and the processor executes the computer program / instruction to realize the vital sign detection method in the embodiment of the application.

[0105] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes according to programs and / or data stored in a read-only memory (ROM) or programs and / or data loaded from a storage section into a random access memory (RAM). The processor can be one multi-core processor or can include a plurality of processors. In some embodiments, the processor can include a general-purpose main processor and one or more special-purpose co-processors, such as a central processing unit, a graphics processing unit (GPU), a neural processing unit (NPU), a digital signal processor (DSP), and the like. In the RAM, various programs and data required for device operations are also stored. The processor, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0106] The above processor and memory are used together to execute programs / instructions stored in the memory, which, when executed by a computer, can implement the methods, steps, or functions described in the above embodiments.

[0107] Although not shown, the embodiments of the present application also provide a computer-readable storage medium having stored thereon computer programs / instructions, which, when executed by a processor, implement the vital sign detection method in the embodiments of the present application.

[0108] The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media do not include transitory media such as modulated data signals and carriers.

[0109] The above only discloses specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or modifications within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A radar-based method for detecting vital signs, characterized in that, The detection method includes: Acquire radar echoes; Target detection is performed on the radar echo to obtain the real target, its range gate, and breathing frequency; The phase signal of the real target's range gate is normalized, superimposed, and dewound with the phase signals of the adjacent range gates of the real target's range gate to obtain the real target's vital signs signal; The target heart rate is obtained by performing Chrip-Z expansion on the heart rate frequency band of vital signs signals and searching for the maximum peak frequency. Target detection of the radar echo includes: The slow-time signal of the radar echo is subjected to a fast Fourier transform to obtain the signal spectrum; Amplitude normalization and threshold-based peak frequency search within the respiratory band are performed on the signal spectrum to obtain potential targets, their distance gates, and respiratory frequencies. Clutter suppression and two-dimensional fast Fourier transform were performed on the radar echo to obtain the echo range-Doppler spectrum; Zero-channel digital beamforming imaging is performed on the echo distance-Doppler spectrum to obtain the echo distance-angle spectrum; CFAR detection was performed on the echo distance-angle spectrum; Based on the potential target and its distance gate, and the CFAR detection results, the real target, its distance gate, and respiratory rate are obtained; Zero-channel digital beamforming imaging of the echo range-Doppler spectrum includes: Extract Doppler zero-velocity channel data from the echo distance-Doppler spectrum; Multi-channel Doppler zero-velocity channel data is used as input for digital beamforming imaging and multiplied by a guide vector array to obtain the echo range-angle spectrum. The guide vector array is composed of guide vectors from different antenna elements and different angle beams, and the formula for generating the guide vectors is: ； in, Indicates the guide vector; Indicates the azimuth coordinates of the antenna array element; Indicates the azimuth angle of the beam direction; , Indicates the radar wavelength; The specific formula for calculating the threshold value of the CFAR detection is as follows: ； ； Where T represents the threshold value; G represents the product factor; G represents the number of reference units. This represents the value of the i-th reference cell; This indicates the expected probability of a false alarm.

2. The radar-based vital sign detection method according to claim 1, characterized in that, Before performing a fast Fourier transform on the slow-time signal of the radar echo, the detection method further includes: The slow-time signal of the radar echo is low-pass filtered; The slow-time signal after low-pass filtering is self-convolved to obtain the self-convolved slow-time signal.

3. The radar-based vital sign detection method according to claim 1, characterized in that, The sliding window averaging cancellation method is used to suppress clutter in the radar echo. The specific formula is as follows: ； ； in, This represents the radar echo of the r-th frame after clutter suppression. This represents the radar echo of the r-th frame; The background data estimate is represented by Q; the accumulated frame count is represented by Q. This represents the radar echo of the r-qth frame.

4. The radar-based vital sign detection method according to any one of claims 1 to 3, characterized in that, Chrip-Z expansion was performed on the heart rate frequency band of vital signs signals, including: Determine the spiral sampling parameters based on the heartbeat frequency band; The spiral sampling parameters are substituted into the Z-transform of the vital signs signal to achieve Chrip-Z expansion.

5. An electronic device comprising a memory, a processor, and a computer program / instructions stored in the memory, characterized in that, The processor executes the computer program / instructions to implement the vital signs detection method as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the vital signs detection method as described in any one of claims 1 to 4.

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