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

Abstract

The invention discloses a radar-based vital sign detection method and device and a storage medium, and the detection method comprises the steps: carrying out the target detection of radar echoes, and obtaining a real target, a range gate where the real target is located, and the respiratory rate; performing normalized superposition and unwrapping on the phase signal of the distance gate where the real target is located and the phase signal of the distance gate adjacent to the distance gate where the real target is located to obtain a vital sign signal of the real target; and performing Chrip-Z expansion on the heartbeat frequency band of the vital sign signal, and performing maximum peak frequency search to obtain a target heart rate. According to the method, normalization fusion is carried out on phase signals of the multiple range gates, the signal-to-noise ratio of respiration and heartbeat signals is increased, and vital sign information carried by radar echoes is fully utilized; high-resolution spectrum analysis in a heartbeat frequency band is realized through Chrip-Z expansion, the heartbeat signal spectrum is refined, and the detection precision of vital sign parameters is improved.

Description

Technical Field

[0001] The present invention belongs to the field of radar technology, and in particular relates to a radar-based vital sign detection method, device and storage medium. Background Art

[0002] The existing radar vital signs detection methods have the following technical problems:

[0003] (1) Target detection and positioning are usually achieved after clutter suppression using a single technique, such as constant false alarm rate (CFAR) or range FFT slow-time signal variance (or energy). However, in complex environments, clutter suppression cannot completely cancel static clutter, making these detection methods prone to false alarms and affecting radar performance.

[0004] (2) The target vital signs signal is realized by extracting the phase signal at the target's strongest distance frequency point. However, the human chest echo covers multiple distance frequencies, all of which contain the target vital signs signal. Therefore, the radar echo signal is not fully utilized.

[0005] (3) Conventional Fourier transform (FFT) is suitable for analyzing the spectrum of the entire sampled signal, but lacks refinement within a certain frequency range, resulting in poor accuracy in detecting target vital sign parameters. In radar vital sign detection, only the human breathing and heartbeat frequency ranges (e.g., 0.1-0.6 Hz, 1.0-2.0 Hz) are usually considered. Summary of the Invention

[0006] The present invention aims to provide a radar-based vital sign detection method, device, and storage medium to address at least one of the following problems: conventional techniques analyze the entire sampling spectrum, resulting in low detection accuracy of target vital sign parameters; clutter suppression cannot completely cancel static clutter, resulting in false alarms; and insufficient utilization of radar echo signals.

[0007] The present invention solves the above technical problems through the following technical solutions: a radar-based vital sign detection method, comprising:

[0008] Acquire radar echoes;

[0009] Performing target detection on the radar echo to obtain the real target, its range gate and breathing frequency;

[0010] The phase signal of the range gate where the real target is located is normalized, superimposed, and de-wrapped with the phase signal of the range gate adjacent to the range gate where the real target is located to obtain the vital sign signal of the real target;

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

[0012] Furthermore, performing target detection on the radar echo includes:

[0013] Performing a fast Fourier transform on the slow-time signal of the radar echo to obtain a signal spectrum;

[0014] The signal spectrum is amplitude normalized and the peak frequency search based on the threshold within the respiratory frequency band is performed to obtain the potential target and its range gate and respiratory frequency.

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

[0016] Performing zero-channel digital beamforming imaging on the echo range-Doppler spectrum to obtain an echo range-angle spectrum;

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

[0018] According to the potential target and its range gate and CFAR detection results, the real target and its range gate and breathing frequency are obtained.

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

[0020] performing low-pass filtering on the slow-time signal of the radar echo;

[0021] The low-pass filtered slow-time signal is subjected to self-convolution to obtain a self-convolved slow-time signal.

[0022] Furthermore, a sliding window average cancellation method is used to suppress clutter on the radar echo. The specific formula is:

[0023] ;

[0024] ;

[0025] in, represents the radar echo of the rth frame after clutter suppression; represents the radar echo of the rth frame; represents the estimated value of background data; Q represents the number of accumulated frames; Indicates the radar echo of the r-qth frame.

[0026] Furthermore, performing zero-channel digital beamforming imaging on the echo range-Doppler spectrum includes:

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

[0028] Multi-channel Doppler zero-velocity channel data is used as the input for digital beamforming imaging and multiplied by the steering vector array to obtain the echo range-angle spectrum. The steering vector array is composed of steering vectors for different antenna elements and different angle beams. The generation formula of the steering vector is:

[0029] ;

[0030] in, represents the steering vector; Indicates the azimuth coordinates of the antenna array element; Indicates the azimuth angle of the beam pointing; , Indicates the radar wavelength.

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

[0032] ;

[0033] ;

[0034] Wherein, T represents the threshold value; represents the multiplication factor; G represents the number of reference units; represents the i-th reference unit value; represents the expected false alarm probability.

[0035] Furthermore, the heartbeat frequency band of the vital sign signal is subjected to Chrip-Z expansion, including:

[0036] Determine spiral sampling parameters according to the heartbeat frequency band;

[0037] Substituting the spiral sampling parameters into the Z transform of the vital sign signal, Chrip-Z expansion is achieved.

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

[0039] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the vital sign detection method described above is implemented.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] This invention normalizes and fuses the phase signals of multiple range gates, improving the signal-to-noise ratio of respiratory and heartbeat signals. This fully utilizes the vital sign information carried by radar echoes, thereby improving the detection accuracy of vital sign parameters. Chrip-Z expansion enables high-resolution spectrum analysis within the heartbeat frequency band, refining the heartbeat signal spectrum and improving the detection accuracy of vital sign parameters. This invention also combines slow-time domain target detection with CFAR target detection for composite judgment, reducing false alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 is a flow chart of a method for detecting vital signs in an embodiment of the present invention;

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

[0045] Figure 3 1 is a comparison diagram of the normalized superposition of signal amplitudes of a single range gate and multiple range gates in an embodiment of the present invention;

[0046] Figure 4 1 is a schematic diagram of Chrip-Z expansion of a heartbeat frequency band according to an embodiment of the present invention;

[0047] Figure 5 4 is a schematic diagram of searching for the maximum peak frequency within the heartbeat frequency band in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following is a clear and complete description of the technical solutions of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0049] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0050] Example 1

[0051] like Figure 1As shown, the radar-based vital sign detection method provided by the embodiment of the present invention includes the following steps:

[0052] Step 1: Get radar echo.

[0053] Step 2: Target detection is performed on the slow-time signal of the radar echo to obtain potential targets, their range gates, and breathing frequencies.

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

[0055] Step 4: Based on the potential target and its range gate and the CFAR detection results, the real target and its range gate and breathing frequency are obtained;

[0056] Step 5: Normalize and superimpose the phase signal of the range gate where the real target is located and the phase signal of the adjacent range gate of the range gate where the real target is located, and then detangle them to obtain the 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 search for the maximum peak frequency to obtain the target heart rate.

[0058] In a specific embodiment of the present invention, in step 2, performing target detection on the slow-time signal of the radar echo includes:

[0059] Step 2.1: Perform a 2 Hz low-pass filter on the slow-time signal of the radar echo to remove high-frequency signal interference. The slow-time signal refers to the time-varying sequence of echo signals received by the same range gate within different pulse transmission cycles.

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

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

[0062] , (1)

[0063] in, 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 both represent the sampling point numbers.

[0064] Figure 2 The amplitude comparison diagram of the signal before and after self-convolution is shown. Figure 2It can be seen that after signal autoconvolution, the signal-to-noise ratio of the breathing frequency of 0.35Hz and the heart rate of 1.5Hz are improved by about 20dB and 8dB respectively compared with before.

[0065] Step 2.3: Perform fast Fourier transform on the slow-time signal after self-convolution to obtain the signal spectrum.

[0066] Step 2.4: Perform amplitude normalization on the signal spectrum and perform a threshold-based peak frequency search within the respiratory frequency band to obtain potential targets, their range gates, and respiratory frequencies.

[0067] The normal human respiratory frequency band is 0.1-0.6 Hz, so a threshold-based peak frequency search is performed within this range. If a peak frequency with an amplitude exceeding the amplitude threshold is detected, a potential target exists in that range gate. Multiple peak frequencies exceeding the amplitude threshold may exist, with the maximum peak frequency being the potential target's respiratory frequency. If no peak frequency exceeding the amplitude threshold is detected, a potential target does not exist in that range gate, and slow-time signal detection proceeds to the next range gate. In this embodiment, the amplitude threshold is set to 0.8.

[0068] In a specific embodiment of the present invention, in step 3, performing target detection on the radar echo includes:

[0069] Step 3.1: Perform clutter suppression on the radar echo.

[0070] In this embodiment, a sliding window average cancellation method is used to suppress clutter on radar echoes. The specific formula is:

[0071] (2)

[0072] (3)

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

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

[0075] The radar echo after clutter suppression is subjected to fast Fourier transform along the range dimension and velocity dimension respectively to obtain the echo range-Doppler spectrum.

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

[0077] Step 3.3: Perform zero-channel digital beamforming imaging on the echo range-Doppler spectrum to obtain the echo range-angle spectrum.

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

[0079] (4)

[0080] in, represents the steering vector; Indicates the azimuth coordinates of the antenna array element; Indicates the azimuth angle of the beam pointing; , Indicates the radar wavelength.

[0081] Assuming that the number of antenna array elements is 8, the required beam angle is -30°~30°, and there is one beam every 1 degree, then the number of angular beams is 61 (including 0°). An 8×61 steering vector array can be generated by formula (4).

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

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

[0084] (5)

[0085] (6)

[0086] Wherein, T represents the threshold value; represents the multiplication factor; represents the average value of the reference unit; G represents the number of reference units; Represents the value of the i-th reference unit (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 there is a target in the detection unit. In this embodiment, the expected false alarm probability is set to 1e -6 ,According to the requirements of low resolution and high real-time performance of ,bioradar, the number of reference units is set to 16.

[0088] In complex scenarios, clutter suppression is ineffective, making CFAR detection prone to false alarms. To reduce false alarms, in step 4, the potential target and its range gate are jointly identified with the CFAR detection results. When a potential target (i.e., the target detected in step 2) and a CFAR-detected target (i.e., the target detected in step 3) are present in the same range gate, a true target is present in that range gate, and the breathing frequency of the potential target is the true target's breathing frequency.

[0089] The slow-time signal spectrum carries the target vital sign frequency (the respiratory signal frequency has the strongest amplitude), while the static clutter frequency that is incompletely canceled after clutter suppression is DC. This feature can be combined with CFAR detection for joint discrimination to reduce false alarms.

[0090] The target's breathing and heartbeat signals are periodic and have relatively stable signal strengths, while the noise is random. In step 5, the phase signal of the range gate where the real target is located is normalized, superimposed, and unwrapped with the phase signal of the adjacent range gate of the range gate where the real target is located. By using multiple range gates, the signal-to-noise ratio of the weak heartbeat signal in the vital sign signal can be improved, and the vital sign information carried by the radar echo can be fully utilized. Figure 3 The comparison of the signal amplitude of a single range gate and a normalized superposition of multiple range gates is shown. 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 a specific embodiment of the present invention, in step 6, performing Chrip-Z expansion on the heartbeat frequency band of the vital sign signal includes:

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

[0093] The spiral sampling parameters are:

[0094] (7)

[0095] in, Indicates taking k sampling points on the Z transform plane; Indicates the radius length of the Z-transform plane where the sampling point is located; represents the starting phase angle; represents the helix stretch rate, Greater than 1 means indentation. Less than 1 indicates extension; Indicates the bisection angle between sampling points on the spiral line, which is determined by (expansion end frequency - start frequency) / (signal sampling rate * number of refinement points); , , Indicates the number of refinement points.

[0096] In this embodiment, the Z-transform plane is a unit circle, and the heartbeat frequency band is 1.0-2.0 Hz. Therefore: , , , , Represents the sampling frequency of the slow-time signal, and M is set to 500.

[0097] Step 6.2: Substitute the spiral sampling parameters into the Z transform of the vital sign signal to achieve the Chrip-Z expansion of the heartbeat frequency band:

[0098] (8)

[0099] in, Represents the Z transform of the vital sign signal; Indicates vital signs signal; Indicates the length of the vital sign signal. , , , then Figure 4 As shown, Chrip-Z expansion can be expressed by convolution as:

[0100] (9)

[0101] The maximum peak frequency is searched for in the heartbeat frequency band of the vital sign signal, and the maximum peak frequency is the target heart rate. Figure 5 As shown, the maximum peak frequency is 1.53Hz, that is, 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, achieving high-resolution spectrum analysis of any frequency range, refining the heartbeat signal frequency band, and extracting target vital sign parameters more precisely.

[0103] Example 2

[0104] An embodiment of the present invention further provides an electronic device, which includes: a memory, a processor, and a computer program / instruction stored in the memory, and the processor executes the computer program / instruction to implement the vital sign detection method in the embodiment of the present invention.

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

[0106] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0107] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the vital sign detection method in the embodiment of the present invention is implemented.

[0108] Computer-readable storage media include permanent and non-permanent, removable and non-removable media that 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0109] The above disclosure is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, and they should all be covered by the scope of protection of the present invention.

Claims

1. A radar-based vital sign detection method, characterized in that: The detection method comprises: Acquire radar echoes; Performing target detection on the radar echo to obtain the real target, its range gate and breathing frequency; The phase signal of the range gate where the real target is located is normalized, superimposed, and de-wrapped with the phase signal of the range gate adjacent to the range gate where the real target is located to obtain the vital sign signal of the real target; Perform Chrip-Z expansion on the heartbeat frequency band of the vital sign signal and search for the maximum peak frequency to obtain the target heart rate.

2. The radar-based vital sign detection method according to claim 1, characterized in that: Performing target detection on the radar echo, including: Performing a fast Fourier transform on the slow-time signal of the radar echo to obtain a signal spectrum; The signal spectrum is amplitude normalized and the peak frequency search based on the threshold within the respiratory frequency band is performed to obtain the potential target and its range gate and respiratory frequency. performing clutter suppression and two-dimensional fast Fourier transform on the radar echo to obtain an echo range-Doppler spectrum; Performing zero-channel digital beamforming imaging on the echo range-Doppler spectrum to obtain an echo range-angle spectrum; Performing CFAR detection on the echo range-angle spectrum; According to the potential target and its range gate and CFAR detection results, the real target and its range gate and breathing frequency are obtained.

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

4. The radar-based vital sign detection method according to claim 2, characterized in that: The sliding window average cancellation method is used to suppress clutter on the radar echo. The specific formula is: ； ； in, represents the radar echo of the rth frame after clutter suppression; represents the radar echo of the rth frame; represents the estimated value of background data; Q represents the number of accumulated frames; Indicates the radar echo of the r-qth frame.

5. The radar-based vital sign detection method according to claim 2, characterized in that: Performing zero-channel digital beamforming imaging on the echo range-Doppler spectrum includes: Extracting Doppler zero-velocity channel data from the echo range-Doppler spectrum; Multi-channel Doppler zero-velocity channel data is used as the input for digital beamforming imaging and multiplied by the steering vector array to obtain the echo range-angle spectrum. The steering vector array is composed of steering vectors for different antenna elements and different angle beams. The generation formula of the steering vector is: ； in, represents the steering vector; Indicates the azimuth coordinates of the antenna array element; Indicates the azimuth angle of the beam pointing; , Indicates the radar wavelength.

6. The radar-based vital sign detection method according to claim 2, characterized in that: The specific calculation formula of the threshold value of the CFAR detection is: ； ； Wherein, T represents the threshold value; represents the multiplication factor; G represents the number of reference units; represents the i-th reference unit value; represents the expected false alarm probability.

7. The radar-based vital sign detection method according to any one of claims 1 to 6, characterized in that: Perform Chrip-Z expansion on the heartbeat frequency band of the vital sign signal, including: Determine spiral sampling parameters according to the heartbeat frequency band; Substituting the spiral sampling parameters into the Z transform of the vital sign signal, Chrip-Z expansion is achieved.

8. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the vital sign detection method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the vital sign detection method according to any one of claims 1 to 7 is implemented.

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