Direct-current bias removal method and device for sign radar signals and computer equipment

Abstract

The invention relates to a direct current bias removal method and device for a sign radar signal and computer equipment, and the method comprises the steps: determining an original phase sequence of signal data at a target position, and a phase unwrapping sequence corresponding to the original phase sequence; determining a phase change range based on the original phase sequence and the phase unwrapping sequence; processing the first complex sequence of the signal data based on a debiasing operation matched with the phase change range to obtain a second complex sequence; the complex sequence comprises amplitude change and phase change of a distance interval where the target position is located on the slow time dimension; and determining a target phase sequence corresponding to the second complex sequence. Through the method and the device, the problem that direct current interference cannot be effectively removed due to the fact that all phase change ranges generated by respiration of different measured targets cannot be adapted is solved, and all phase change ranges generated by respiration of different measured targets can be adapted to effectively remove direct current interference.

Description

Technical Field

[0001] The present application relates to the field of radar technology, and particularly to a method, device, and computer equipment for removing the DC bias of a vital sign radar signal. Background Art

[0002] For regular micro-motions with small amplitudes, a high-frequency millimeter-wave radar can continuously monitor and accumulate observations of the echo signal over a long period of time, and use corresponding signal processing algorithms to statistically calculate the frequency of the micro-motion. Therefore, it is often applied to non-contact vital sign detection, such as using a millimeter-wave radar to monitor human respiration, heartbeat, and other characteristics. Among them, when using a millimeter-wave radar to monitor respiration or heartbeat, it is necessary to perform a DC removal operation on the input signal to exclude the interference of the DC component.

[0003] In the existing phase sequence processing method, usually, a moving average filter is performed on the extracted slow-time phase array to obtain a phase trend line, and the phase array is subtracted from the phase trend line to filter out the DC interference in the signal. However, the above method cannot adapt to all phase change ranges generated by the respiration of different measured targets, resulting in the inability to effectively remove DC interference.

[0004] Regarding the problem in the related technology that it is impossible to adapt to all phase change ranges generated by the respiration of different measured targets, resulting in the inability to effectively remove DC interference, no effective solution has been proposed yet. Summary of the Invention

[0005] In this embodiment, a method, device, and computer equipment for removing the DC bias of a vital sign radar signal are provided to solve the problem in the related technology that it is impossible to adapt to all phase change ranges generated by the respiration of different measured targets, resulting in the inability to effectively remove DC interference.

[0006] In a first aspect, in this embodiment, a method for removing the DC bias of a vital sign radar signal is provided, and the method includes:

[0007] Determine the original phase sequence of the signal data at the target position and the phase unwrapping sequence corresponding to the original phase sequence;

[0008] Based on the original phase sequence and the phase unwrapping sequence, determine the corresponding phase change range;

[0009] Based on a debiasing operation that matches the phase change range, process the first complex sequence of the signal data to obtain a second complex sequence; the complex sequence includes the amplitude change and phase change in the distance interval where the target position is located in the slow-time dimension;

[0010] Determine the target phase sequence corresponding to the second complex sequence.

[0011] In some of these embodiments, determining the original phase sequence of the signal data at the target position includes:

[0012] Obtaining the first complex sequence of the signal data;

[0013] Performing a de - mean operation on the first complex sequence;

[0014] Calculating the phase values of the de - meaned first complex sequence to obtain the original phase sequence of the signal data at the target position.

[0015] In some of these embodiments, determining the phase unwrapping sequence corresponding to the original phase sequence includes:

[0016] Processing the original phase sequence through a phase unwrapping function to obtain the phase unwrapping sequence corresponding to the original phase sequence.

[0017] In some of these embodiments, based on the original phase sequence and the phase unwrapping sequence, determining the corresponding phase change range includes:

[0018] Performing spectral analysis on the band - pass filtered original phase sequence and the phase unwrapping sequence respectively to obtain a first spectrum corresponding to the original phase sequence and a second spectrum corresponding to the phase unwrapping sequence;

[0019] Determining a first maximum amplitude in the first spectrum and a second maximum amplitude in the second spectrum;

[0020] Based on the comparison result of the first maximum amplitude and the second maximum amplitude, determining the target sequence in the original phase sequence and the phase unwrapping sequence;

[0021] Based on the target sequence, determining the corresponding phase change range.

[0022] In some of these embodiments, selecting a de - biasing operation that matches the phase change range includes:

[0023] Comparing the phase change range with a preset threshold;

[0024] According to the comparison result, selecting the de - biasing operation that matches the phase change range.

[0025] In some of these embodiments, when the phase change range is greater than the preset threshold, based on the de - biasing operation that matches the phase change range, processing the first complex sequence of the signal data to obtain a second complex sequence, includes:

[0026] Process the first complex sequence after mean removal through a circle fitting algorithm to obtain the corresponding center coordinates;

[0027] Based on the center coordinates, determine the corresponding first DC baseline offset;

[0028] Based on the first DC baseline offset and the first complex sequence after mean removal, determine the second complex sequence.

[0029] In some embodiments, when the phase change range is less than or equal to the preset threshold, processing the first complex sequence of the signal data based on a debiasing operation matching the phase change range to obtain a second complex sequence includes:

[0030] Perform low-pass filtering on the original phase sequence to obtain the corresponding low-pass phase sequence;

[0031] According to multiple preset radii, the mean position of the upper boundary points and the mean position of the lower boundary points corresponding to the low-pass phase sequence, determine multiple center coordinates;

[0032] Based on the best center coordinate among the center coordinates, determine the corresponding second DC baseline offset;

[0033] Based on the second DC baseline offset and the first complex sequence after mean removal, determine the second complex sequence.

[0034] In some embodiments, determining the best center coordinate among the center coordinates includes:

[0035] Traverse each of the center coordinates, and based on each center coordinate, determine the spectral amplitude sequence corresponding to the first complex sequence after mean removal;

[0036] Based on the spectral amplitude sequence, the maximum value of the spectral amplitude sequence, and the index corresponding to the maximum value, determine the corresponding target value; the target value is the ratio of the respiratory harmonic frequency energy to the fundamental frequency energy;

[0037] Take the center coordinate corresponding to the minimum target value as the best center coordinate.

[0038] In a second aspect, in the present embodiment, a DC bias removal device for a vital sign radar signal is provided, and the device includes: a first operation module, a determination module, a processing module, and a second operation module;

[0039] The first operation module is configured to determine the original phase sequence of the signal data at the target position and the phase unwrapping sequence corresponding to the original phase sequence;

[0040] The determining module is configured to determine a corresponding phase change range based on the original phase sequence and the phase unwrapping sequence.

[0041] The processing module is configured to process the first complex sequence of the signal data based on a debiasing operation matching the phase change range to obtain a second complex sequence; the complex sequence includes the amplitude change and the phase change of the distance interval where the target position is located in the slow time dimension.

[0042] The second operation module is configured to determine a target phase sequence corresponding to the second complex sequence.

[0043] In a third aspect, a computer device is provided in this embodiment, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for removing the DC bias of the vital sign radar signal described in the first aspect above is implemented.

[0044] In a fourth aspect, a storage medium is provided in this embodiment, on which a computer program is stored. When the program is executed by a processor, the method for removing the DC bias of the vital sign radar signal described in the first aspect above is implemented.

[0045] Compared with the related art, the method, device, and computer device for removing the DC bias of the vital sign radar signal provided in this embodiment determine the original phase sequence of the signal data at the target position and the phase unwrapping sequence corresponding to the original phase sequence; determine the corresponding phase change range based on the original phase sequence and the phase unwrapping sequence; process the first complex sequence of the signal data based on a debiasing operation matching the phase change range to obtain a second complex sequence; the complex sequence includes the amplitude change and the phase change of the distance interval where the target position is located in the slow time dimension; determine the target phase sequence corresponding to the second complex sequence, solving the problem that it is impossible to adapt to all phase change ranges generated by the breathing of different measured targets, resulting in the inability to effectively remove the DC interference, and realizing adapting to all phase change ranges generated by the breathing of different measured targets to effectively remove the DC interference.

[0046] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0048] Figure 1It is a hardware structure block diagram of a terminal device for a method of removing DC bias from a vital sign radar signal provided by an embodiment of the present application;

[0049] Figure 2 It is a flowchart of a method of removing DC bias from a vital sign radar signal provided by an embodiment of the present application;

[0050] Figure 3 It is a schematic diagram of unwrapping processing provided by an embodiment of the present application;

[0051] Figure 4 It is a schematic diagram of unwrapping processing provided by another embodiment of the present application;

[0052] Figure 5 It is a schematic diagram of a method for subdividing and constructing the center of a circle provided by an embodiment of the present application;

[0053] Figure 6 It is a schematic flowchart of a method of removing DC bias from a vital sign radar signal provided by an embodiment of the present application;

[0054] Figure 7 It is a flowchart of a method of removing DC bias from a vital sign radar signal provided by a preferred embodiment of the present application;

[0055] Figure 8 It is a structure block diagram of a device for removing DC bias from a vital sign radar signal provided by an embodiment of the present application.

[0056] In the figure: 102, processor; 104, memory; 106, transmission device; 108, input / output device; 10, first operation module; 20, determination module; 30, processing module; 40, second operation module. Detailed implementation manners

[0057] To more clearly understand the purpose, technical solution and advantages of the present application, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments.

[0058] Unless otherwise defined, technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those of ordinary skill in the technical field to which this application belongs. In this application, words such as "a", "an", "one kind", "the", "these", etc. do not indicate a limitation in quantity, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent in these processes, methods, products or devices. The terms "connected", "coupled" and other similar words involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly connected. The term "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist simultaneously, and B exists alone. Usually, the character " / " indicates that the objects associated before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific sorting of the objects.

[0059] The method embodiment provided in this embodiment can be executed on a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 is a hardware structure block diagram of the terminal for the method of removing the DC bias of the vital sign radar signal in this embodiment. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 and a memory 104 for storing data. Among them, the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0060] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the DC bias removal method of the vital sign radar signal in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above-mentioned method is implemented. The memory 104 may include a high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely provided with respect to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0061] The transmission device 106 is used to receive or send data via a network. The above-mentioned network includes the wireless network provided by the communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0062] In this embodiment, a method for removing the DC bias of the vital sign radar signal is provided. Figure 2 is the flowchart of the method for removing the DC bias of the vital sign radar signal in this embodiment, as Figure 2 shown, this process includes the following steps:

[0063] Step S210, determine the original phase sequence of the signal data at the target position, and the phase unwrapping sequence corresponding to the original phase sequence;

[0064] Step S220, based on the original phase sequence and the phase unwrapping sequence, determine the corresponding phase change range;

[0065] Step S230, based on the debiasing operation matching the phase change range, process the first complex sequence of the signal data to obtain a second complex sequence; the complex sequence includes the amplitude change and phase change in the distance interval where the target position is located in the slow time dimension;

[0066] Step S240, determine the target phase sequence corresponding to the second complex sequence.

[0067] Specifically, when using a millimeter-wave radar to monitor human respiration, heartbeat and other characteristics, the received echo signal is sampled by an Analog to Digital Converter (ADC) to obtain ADC data. The ADC data is subjected to a one-dimensional fast Fourier transform operation to obtain corresponding range dimension data, which reflects the reflection intensity distribution of the target at different ranges. Accumulatively acquire N sets of range dimension data in the slow time dimension to capture the range change characteristics of the target (such as the human chest cavity) over a period of time, and detect and determine the target position. Extract a first complex sequence corresponding to the range interval where the target position is located from the N sets of range dimension data. The size of the first complex sequence is N×1, and this sequence includes the amplitude change and phase change of the range interval where the target position is located in the slow time dimension. Among them, the slow time dimension refers to the time scale composed of multiple chirp signal periods, and N is the number of sampling points in the slow time, usually 256 or 512.

[0068] Further, perform a mean removal operation on the first complex sequence, calculate the phase value of the mean-removed first complex sequence to obtain the original phase sequence of the signal data at the target position, and calculate the original phase sequence through a phase unwrapping function to obtain a phase unwrapped sequence corresponding to the original phase sequence. Perform band-pass filtering on the original phase sequence and the phase unwrapped sequence, perform spectral analysis on the filtered original phase sequence and phase unwrapped sequence respectively to obtain a first spectrum corresponding to the original phase sequence and a second spectrum corresponding to the phase unwrapped sequence. Determine the first index number corresponding to the maximum amplitude in the first spectrum and the second index number corresponding to the maximum amplitude in the second spectrum, and determine the target sequence in the original phase sequence and the phase unwrapped sequence according to the comparison result of the first index number and the second index number. Perform low-pass filtering on the target sequence to exclude high-frequency glitches to obtain a corresponding low-pass phase sequence, and then calculate the phase change range according to the low-pass phase sequence.

[0069] Compare the phase change range with a preset threshold, and select a debiasing operation adapted to the phase change range according to the comparison result. When the phase change range is greater than the preset threshold, process the first complex number sequence after mean removal through a circle fitting algorithm to obtain the corresponding center coordinates, calculate the corresponding first DC baseline bias according to the center coordinates, and subtract the first DC baseline bias from the first complex number sequence after mean removal to obtain a second complex number sequence; otherwise, if the phase change range is less than or equal to the preset threshold, perform low-pass filtering on the original phase sequence to obtain a low-pass phase sequence, calculate multiple center coordinates according to multiple preset radii, the mean position of the upper boundary points and the mean position of the lower boundary points corresponding to the low-pass phase sequence, screen out the optimal center coordinates from each center coordinate, calculate the corresponding second DC baseline bias according to the optimal center coordinates, and subtract the second DC baseline bias from the first complex number sequence after mean removal to obtain a second complex number sequence. Then, perform operations such as phase calculation, unwrapping, and phase mean removal on the second complex number sequence to obtain a target phase sequence with DC baseline bias removed.

[0070] In the existing phase sequence processing methods, usually perform moving average filtering on the extracted slow-time phase array to obtain a phase trend line, and subtract the phase trend line from the phase array to filter out the DC interference in the signal. However, the above method cannot adapt to all phase change ranges generated by the breathing of different measured targets, resulting in the inability to effectively remove DC interference.

[0071] Compared with the prior art, the present application determines the original phase sequence of the signal data at the target position and the corresponding phase unwrapping sequence of the original phase sequence; based on the original phase sequence and the phase unwrapping sequence, determines the corresponding phase change range; based on a debiasing operation matching the phase change range, processes the first complex number sequence of the signal data to obtain a second complex number sequence; the complex number sequence includes the amplitude change and phase change in the distance interval where the target position is located in the slow-time dimension; determines the target phase sequence corresponding to the second complex number sequence. Based on this, by pre-calculating the phase change range of the signal and matching the corresponding debiasing operation according to the phase change range, it is possible to use an adapted debiasing operation to process and obtain a phase sequence with DC baseline bias removed, solving the problem that it is impossible to adapt to all phase change ranges generated by the breathing of different measured targets, resulting in the inability to effectively remove DC interference, and achieving adaptation to all phase change ranges generated by the breathing of different measured targets to effectively remove DC interference.

[0072] In some of these embodiments, determining the original phase sequence of the signal data in step S210 includes the following steps:

[0073] Step S211, obtaining the first complex number sequence of the signal data;

[0074] Step S212, perform a mean removal operation on the first complex sequence;

[0075] Step S213, calculate the phase values of the mean-removed first complex sequence to obtain the original phase sequence of the signal data at the target position.

[0076] Specifically, accumulate and obtain N sets of range dimension data in the slow time dimension, where N is the number of slow time sampling points, usually 256 or 512. From the N sets of range dimension data, extract the first complex sequence corresponding to the range interval where the target position is located. The size of the first complex sequence is N×1, and this sequence includes the amplitude change and phase change in the range interval where the target position is located in the slow time dimension.

[0077] Further, perform a mean removal operation on the first complex sequence. The specific formula is as follows:

[0078]

[0079] In Equation (1), SigCplxNoOffset represents the mean-removed first complex sequence; SigCplx represents the first complex sequence; and N is the number of slow time sampling points.

[0080] Perform a phase value calculation on the mean-removed first complex sequence. The phase return value is between -π and π to obtain the original phase sequence of the signal data at the target position, with a size of N×1. The specific formula is as follows:

[0081] PhaseOrigin = atan2(imag(SigCplxNoOffset), real(SigCplxNoOffset)) (2)

[0082] In Equation (2), PhaseOrigin represents the original phase sequence; imag(SigCplxNoOffset) represents the imaginary part of SigCplxNoOffset; and real(SigCplxNoOffset) represents the real part of SigCplxNoOffset.

[0083] Through this embodiment, obtain the first complex sequence of the signal data, perform a mean removal operation on the first complex sequence, and perform a phase value calculation on the mean-removed first complex sequence to obtain the original phase sequence of the signal data at the target position, thereby obtaining the original phase sequence.

[0084] In some of these embodiments, determining the phase unwrapping sequence corresponding to the original phase sequence in step S210 includes the following steps:

[0085] Process the original phase sequence through a phase unwrapping function to obtain the phase unwrapping sequence corresponding to the original phase sequence.

[0086] Specifically, the original phase sequence is operated on by a phase unwrapping function to obtain a phase unwrapped sequence corresponding to the original phase sequence. The specific formula is as follows:

[0087] PhaseUnwrap = unwrap(PhaseOrigin) (3)

[0088] In formula (3), PhaseUnwrap represents the phase unwrapped sequence; unwrap represents the phase unwrapping function; PhaseOrigin represents the original phase sequence. It should be noted that the unwrap function analyzes the difference between adjacent phase values in the original phase sequence, determines whether a phase jump occurs, and corrects the jump so that the phase sequence can continuously reflect the true phase change of the signal.

[0089] Through this embodiment, the original phase sequence is processed by the phase unwrapping function to obtain a phase unwrapped sequence corresponding to the original phase sequence, thereby restoring the true phase change of the signal.

[0090] In some of these embodiments, determining the corresponding phase change range based on the original phase sequence and the phase unwrapped sequence in step S220 includes the following steps:

[0091] Step S221, perform spectral analysis on the original phase sequence and the phase unwrapped sequence after band-pass filtering respectively to obtain a first spectrum corresponding to the original phase sequence and a second spectrum corresponding to the phase unwrapped sequence;

[0092] Step S222, determine a first maximum amplitude in the first spectrum and a second maximum amplitude in the second spectrum;

[0093] Step S223, determine the target sequence in the original phase sequence and the phase unwrapped sequence according to the comparison result of the first maximum amplitude and the second maximum amplitude;

[0094] Step S224, determine the corresponding phase change range based on the target sequence.

[0095] It should be noted that in cases where the breathing characteristics of the measured target are obvious, the chest cavity fluctuates greatly, or the sensor is directly facing the chest of the measured target, etc., the phase change range of the signal is large, and the situation where the phase change exceeds the phase boundary value of the atan2 function will occur, that is, phase ambiguity occurs. At this time, phase unwrapping processing needs to be performed on the signal, such as Figure 3As shown. In cases where the chest movement of the target under measurement is small or the target is facing away from the radar, etc., the phase change range of the signal is small. Affected by the noise in the signal, there will be phase spikes after the complex sequence is de-meaned and de-phased. If the unwrapping operation is directly performed on the basis of the original phase sequence, the phase mutation will cause errors in the unwrapping, as Figure 4 shown.

[0096] Based on this, band-pass filtering is performed on the original phase sequence and the phase unwrapping sequence in advance to filter out high-frequency noise and DC interference. The fast Fourier transform algorithm is used to perform spectrum analysis on the original phase sequence and the phase unwrapping sequence after band-pass filtering respectively, to obtain the first spectrum corresponding to the original phase sequence and the second spectrum corresponding to the phase unwrapping sequence.

[0097] Furthermore, determine the first maximum amplitude and the corresponding index number in the first spectrum, and the second maximum amplitude and the corresponding index number in the second spectrum. The specific expressions are as follows:

[0098] [PUMaxValue, PUMaxIdx] = max(abs(FFT(PhaseUnwrap))) (4)

[0099] [POMaxValue, POMaxIdx] = max(abs(FFT(PhaseOrigin))) (5)

[0100] In formula (4), PUMaxValue represents the first maximum amplitude; PUMaxIdx represents the index number corresponding to the first maximum amplitude; FFT() represents the fast Fourier transform; PhaseUnwrap represents the phase unwrapping sequence; POMaxValue represents the second maximum amplitude; POMaxIdx represents the index number corresponding to the second maximum amplitude; PhaseOrigin represents the original phase sequence.

[0101] Compare the first maximum amplitude with the second maximum amplitude. According to the comparison result, determine the target sequence in the original phase sequence and the phase unwrapping sequence. Among them, if the first maximum amplitude is greater than the second maximum amplitude, select the phase unwrapping sequence as the target sequence; if the second maximum amplitude is greater than the first maximum amplitude, select the original phase sequence as the target sequence; if the first maximum amplitude is equal to the second maximum amplitude, it indicates that the original phase sequence and the phase unwrapping sequence are the same, and select any one of the original phase sequence and the phase unwrapping sequence as the target sequence.

[0102] After that, according to the target sequence, calculate the corresponding phase change range. For example, taking the selected phase unwrapping sequence as the target sequence, perform low-pass filtering on the phase unwrapping sequence to exclude high-frequency glitches, and obtain the low-pass phase sequence. Then, the specific calculation formula for the phase change range is as follows:

[0103] PhaseRange = max(PhaseLP) - min(PhaseLP) (6)

[0104] In formula (6), PhaseRange represents the phase change range, with a size of N×1; PhaseLP represents the low-pass phase sequence.

[0105] Through this embodiment, perform spectral analysis on the original phase sequence and the phase unwrapping sequence after band-pass filtering respectively, to obtain the first spectrum corresponding to the original phase sequence and the second spectrum corresponding to the phase unwrapping sequence. Determine the first maximum amplitude in the first spectrum and the second maximum amplitude in the second spectrum. According to the comparison result of the first maximum amplitude and the second maximum amplitude, determine the target sequence in the original phase sequence and the phase unwrapping sequence, and then based on the target sequence, determine the corresponding phase change range, so as to accurately analyze the current phase change range and facilitate the subsequent selection of a matching debiasing operation.

[0106] In some of these embodiments, selecting a debiasing operation that matches the phase change range includes the following steps:

[0107] Compare the phase change range with a preset threshold;

[0108] According to the comparison result, select a debiasing operation that matches the phase change range.

[0109] Specifically, compare the phase change range with a preset threshold, and select a debiasing operation that is adapted to the phase change range according to the comparison result. Among them, the preset threshold is α*2*π, and α is an adjustable variable.

[0110] When the phase change range is greater than the preset threshold, it indicates that the phase change range can support the circle fitting under the non-linear least squares estimation method. Process the first complex sequence after mean removal through the circle fitting algorithm to obtain the corresponding center coordinates, calculate the corresponding first DC baseline bias according to the center coordinates, and subtract the first DC baseline bias from the first complex sequence after mean removal to obtain the second complex sequence. Conversely, if the phase change range is less than or equal to the preset threshold, it indicates that the phase change range cannot support the data richness required for circle fitting, and using circle fitting is likely to cause phase estimation errors, resulting in the inability to restore the true situation of the corrected phase. At this time, the method of subdividing and constructing the center is used to determine the bias.

[0111] In this embodiment, the phase change range is compared with a preset threshold value. According to the comparison result, a debiasing operation that matches the phase change range is selected, thereby selecting a matching debiasing operation to ensure effective removal of DC interference.

[0112] In some of these embodiments, when the phase change range is greater than the preset threshold value, based on a debiasing operation that matches the phase change range, the first complex sequence of the signal data is processed to obtain a second complex sequence, including the following steps:

[0113] The first complex sequence after mean removal is processed by a circle fitting algorithm to obtain the corresponding center coordinates;

[0114] Based on the center coordinates, the corresponding first DC baseline bias is determined;

[0115] Based on the first DC baseline bias and the first complex sequence after mean removal, the second complex sequence is determined.

[0116] Specifically, when the phase change range is greater than the preset threshold value, the first complex sequence after mean removal is processed by a circle fitting algorithm to fit the center coordinates (CenterReal1, CenterImag1), and based on the center coordinates, the corresponding first DC baseline bias is calculated. The specific formula is as follows:

[0117] DCOffsetValue1 = CenterReal1 + 1i * CenterImag1 (7)

[0118] In formula (7), DCOffsetValue1 represents the first DC baseline bias; (CenterReal1, CenterImag1) are the center coordinates obtained by circle fitting calculation. It should be noted that when the phase change range is greater than the preset threshold value, it indicates that the phase change range can support circle fitting under the non-linear least squares estimation method.

[0119] In this embodiment, the first complex sequence after mean removal is processed by a circle fitting algorithm to obtain the corresponding center coordinates, and the corresponding first DC baseline bias is determined based on the center coordinates, so as to accurately obtain the debiased complex sequence when the phase change range is greater than the preset threshold value, and avoid phase estimation errors.

[0120] In some of these embodiments, when the phase change range is less than or equal to the preset threshold value, based on a debiasing operation that matches the phase change range, the first complex sequence of the signal data is processed to obtain a second complex sequence, including the following steps:

[0121] The original phase sequence is low-pass filtered to obtain the corresponding low-pass phase sequence;

[0122] Determine a plurality of center coordinates according to a plurality of preset radii, the mean position of the upper boundary points corresponding to the low-pass phase sequence, and the mean position of the lower boundary points;

[0123] Based on the optimal center coordinate among the center coordinates, determine the corresponding second DC baseline offset;

[0124] Based on the second DC baseline offset and the first complex sequence after mean removal, determine the second complex sequence.

[0125] Specifically, perform low-pass filtering on the original phase sequence to obtain the corresponding low-pass phase sequence PhaseLP, and determine the boundary point index values of the low-pass phase sequence, including the upper boundary point index and the lower boundary point index of the low-pass phase sequence. The specific expressions are as follows:

[0126] UpIdx = find(PhaseLP > max(PhaseLP) - σ) (8)

[0127] DownIdx = find(PhaseLP < min(PhaseLP) + σ) (9)

[0128] In equations (8) and (9), UpIdx represents the set of upper boundary point indexes, which contains the phase value indexes within σ of all the maximum phase values in the low-pass phase sequence; DownIdx represents the set of lower boundary point indexes, which contains the phase value indexes within σ of all the minimum phase values in the low-pass phase sequence; σ represents the boundary point tolerance.

[0129] Further, determine the mean position of the upper boundary points corresponding to the set of upper boundary point indexes, and the mean position of the lower boundary points corresponding to the set of lower boundary point indexes. The specific expressions are as follows:

[0130]

[0131] In equations (10) and (11), UpCplx represents the mean position of the upper boundary points; DownCplx represents the mean position of the lower boundary points; the function numel() is used to return the number of elements of the input array or set. Among them, both UpCplx and DownCplx are single complex values.

[0132] After that, stepwise construct a plurality of preset radii, and determine a plurality of center coordinates according to each preset radius, the mean position of the upper boundary points, and the mean position of the lower boundary points. Specifically, take UpCplx and DownCplx as the two end vertices of the chord corresponding to the current phase data, and the center of the circle is located on the perpendicular bisector of this chord segment. As Figure 5As shown, the blue circles are the points corresponding to the SigCplxNoOffset sequence, the horizontal axis is the real part, and the vertical axis is the imaginary part; the red boxes are the points corresponding to UpCplx and DownCplx, and the red dotted line is the chord corresponding to the current phase data; the black solid line is the central axis of the chord, and the black circles are the multiple centers of the step structure.

[0133] Among them, UpCplx=x1+y1*1i and DownCplx=x2+y3*1i are preset, and the expression of the corresponding perpendicular bisector line BisectorLine is as follows:

[0134]

[0135] Based on this, according to the radius step r step Determine the coordinate set of the circle center. The specific expression is as follows:

[0136]

[0137] In formula (13), CircleCenter represents the coordinate set of the center of the circle; the function dist((a,b),(c,d)) returns the distance between the input points (a,b) and (c,d); MinD can be set according to the boundary value in SigCplxNoOffset; MaxD can be set according to the phase change range corresponding to the minimum amplitude of the respiratory chest fluctuation, for example, referring to the phase change range corresponding to the minimum amplitude of the respiratory chest fluctuation of 0.1mm; r step It can be obtained by dividing (MinD, MaxD) into M equal parts. The selection of M takes into account the balance between embedded implementation resource usage and accuracy. It should be noted that the above MinD, MaxD and r step The specific values are flexibly set according to actual needs.

[0138] Select the best center coordinates (CenterReal2, CenterImag2) from each center coordinate, and calculate the corresponding second DC baseline offset according to the best center coordinates. The specific formula is as follows:

[0139] DCOffsetValue2=CenterReal2+1i*CenterImag2 (14)

[0140] In formula (14), DCOffsetValue2 represents the second DC baseline offset. The second DC baseline offset is subtracted from the first complex sequence after the mean is removed to obtain the second complex sequence.

[0141] In this embodiment, the original phase sequence is low-pass filtered to obtain a corresponding low-pass phase sequence. According to multiple preset radii, the mean positions of the upper boundary points and the mean positions of the lower boundary points corresponding to the low-pass phase sequence, multiple center coordinates are determined. Based on the optimal center coordinate among the center coordinates, a corresponding second DC baseline offset is determined. Furthermore, based on the second DC baseline offset and the first complex sequence after mean removal, a second complex sequence is determined, so as to accurately obtain the debiased complex sequence when the phase change range is less than or equal to the preset threshold, and avoid phase estimation errors.

[0142] In some of these embodiments, determining the optimal center coordinate among the center coordinates includes the following steps:

[0143] Traverse each center coordinate, and based on each center coordinate, determine the spectral amplitude sequence corresponding to the first complex sequence after mean removal;

[0144] Based on the spectral amplitude sequence, the maximum value of the spectral amplitude sequence, and the index corresponding to the maximum value, determine the corresponding target value; the target value is the proportion of the respiratory harmonic frequency energy to the fundamental frequency energy;

[0145] Take the center coordinate corresponding to the minimum target value as the optimal center coordinate.

[0146] Specifically, traverse each center coordinate. For each center coordinate, subtract the complex value corresponding to the center coordinate from SigCplxNoOffset, and perform operations such as phase calculation, unwrapping, phase mean removal, FFT for spectral calculation, and spectral amplitude sequence calculation on the subtraction result to obtain the spectral amplitude sequence AbsFFTComped corresponding to the center coordinate.

[0147] Further, use the max function to determine the maximum value of the spectral amplitude sequence and the index corresponding to the maximum value, and calculate the target value based on the spectral amplitude sequence, the maximum value of the spectral amplitude sequence, and the index corresponding to the maximum value. The specific calculation formula is as follows:

[0148]

[0149] In Equation (15), ResharmocisPct represents the target value, that is, the proportion of the respiratory harmonic frequency energy to the fundamental frequency energy; ResAmp represents the maximum value of the spectral amplitude sequence; ResIdx represents the index corresponding to the maximum value.

[0150] After that, determine the minimum target value among the target values, and select the center coordinate corresponding to the minimum target value as the optimal center coordinate. It should be noted that the minimum target value indicates that the current respiratory harmonic energy occupies the smallest proportion of the fundamental frequency energy, which is suitable for subsequent processing of sign detection.

[0151] In this embodiment, each center coordinate is traversed, and based on each center coordinate, a spectral amplitude sequence corresponding to the first complex sequence after mean removal is determined.

[0152] Based on the spectral amplitude sequence, the maximum value of the spectral amplitude sequence, and the index corresponding to the maximum value, the corresponding target value is determined. The target value is the ratio of the respiratory harmonic frequency energy to the fundamental frequency energy. Furthermore, the center coordinate corresponding to the minimum target value is taken as the optimal center coordinate, which helps to accurately calculate the DC baseline offset.

[0153] The following describes and illustrates this embodiment through specific examples.

[0154] Figure 6 is a schematic flowchart of the method for removing the DC offset of the vital sign radar signal in this embodiment. As Figure 6 shown, the method for removing the DC offset of the vital sign radar signal specifically includes the following steps:

[0155] Obtain the first complex sequence of the signal data at the target chest, perform a mean removal operation S610 on the first complex sequence, and calculate the phase values of the first complex sequence after mean removal to obtain the original phase sequence of the signal data at the target position S620.

[0156] Furthermore, the original phase sequence is processed by a phase unwrapping function to obtain a phase unwrapped sequence corresponding to the original phase sequence S630. The spectral analysis is respectively performed on the original phase sequence and the phase unwrapped sequence after band-pass filtering to obtain the first spectrum corresponding to the original phase sequence and the second spectrum corresponding to the phase unwrapped sequence S640. Determine the first maximum amplitude in the first spectrum and the second maximum amplitude in the second spectrum. According to the comparison result of the first maximum amplitude and the second maximum amplitude, determine the target sequence in the original phase sequence and the phase unwrapped sequence, and based on the target sequence, determine the corresponding phase change range S650.

[0157] Determine whether the phase change range is greater than a preset threshold S660. When the phase change range is greater than the preset threshold, process the first complex sequence after mean removal through a circle fitting algorithm to obtain the corresponding center coordinates, calculate the corresponding first DC baseline offset according to the center coordinates, and subtract the first DC baseline offset from the first complex sequence after mean removal to obtain a second complex sequence; when the phase change range is less than or equal to the preset threshold, perform low-pass filtering on the original phase sequence to obtain a corresponding low-pass phase sequence, determine multiple center coordinates according to multiple preset radii, the mean position of the upper boundary points and the mean position of the lower boundary points corresponding to the low-pass phase sequence, and calculate the corresponding second DC baseline offset according to the best center coordinates among the center coordinates, and subtract the second DC baseline offset S680 from the first complex sequence after mean removal to obtain a second complex sequence. Then, perform operations such as phase calculation, unwrapping, and phase mean removal on the second complex sequence to obtain a target phase sequence S690 after DC baseline offset removal.

[0158] The following describes and illustrates this embodiment through preferred embodiments.

[0159] Figure 7 is a flowchart of the DC bias removal method for the vital sign radar signal of this preferred embodiment, as Figure 7 shown, the DC bias removal method for the vital sign radar signal includes the following steps:

[0160] Step S710, determine the original phase sequence of the signal data at the target position and the corresponding phase unwrapping sequence;

[0161] Step S720, perform spectrum analysis on the original phase sequence and the phase unwrapping sequence after band-pass filtering respectively to obtain a first spectrum corresponding to the original phase sequence and a second spectrum corresponding to the phase unwrapping sequence;

[0162] Step S730, determine the first maximum amplitude in the first spectrum and the second maximum amplitude in the second spectrum, and determine the target sequence in the original phase sequence and the phase unwrapping sequence according to the comparison result of the first maximum amplitude and the second maximum amplitude;

[0163] Step S740, based on the target sequence, determine the corresponding phase change range, compare the phase change range with the preset threshold, and select a debiasing operation that matches the phase change range according to the comparison result;

[0164] Step S750, based on the debiasing operation that matches the phase change range, process the first complex sequence of the signal data to obtain a second complex sequence;

[0165] Step S760: Perform operations such as phase calculation, unwrapping, and dephasing mean on the second complex sequence to obtain a target phase sequence with the DC baseline offset removed.

[0166] In this embodiment, the original phase sequence of the signal data at the target position and the phase unwrapping sequence corresponding to the original phase sequence are determined. The spectrum analysis is respectively performed on the original phase sequence after band-pass filtering and the phase unwrapping sequence to obtain a first spectrum corresponding to the original phase sequence and a second spectrum corresponding to the phase unwrapping sequence. The first maximum amplitude in the first spectrum and the second maximum amplitude in the second spectrum are determined. According to the comparison result of the first maximum amplitude and the second maximum amplitude, the target sequence in the original phase sequence and the phase unwrapping sequence is determined, and based on the target sequence, the corresponding phase change range is determined.

[0167] Compare the phase change range with a preset threshold, and select a debiasing operation that matches the phase change range according to the comparison result. Based on the debiasing operation that matches the phase change range, process the first complex sequence of the signal data to obtain a second complex sequence, and then perform operations such as phase calculation, unwrapping, and dephasing mean on the second complex sequence to obtain a target phase sequence with the DC baseline offset removed, which solves the problem that it is impossible to adapt to all phase change ranges generated by the breathing of different measured targets, resulting in the inability to effectively remove DC interference, and realizes adapting to all phase change ranges generated by the breathing of different measured targets to effectively remove DC interference.

[0168] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0169] In this embodiment, a DC bias removal device for a vital sign radar signal is also provided. This device is used to implement the above embodiment and the preferred implementation manners, and those that have been described will not be repeated. The following terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0170] Figure 8 is the structural block diagram of the DC bias removal device for the vital sign radar signal of this embodiment, as Figure 8 shown, the device includes: a first operation module 10, a determination module 20, a processing module 30, and a second operation module 40;

[0171] The first operation module 10 is used to determine the original phase sequence of the signal data at the target position and the phase unwrapping sequence corresponding to the original phase sequence;

[0172] The determination module 20 is used to determine the corresponding phase change range based on the original phase sequence and the phase unwrapping sequence;

[0173] The processing module 30 is used to process the first complex sequence of the signal data based on a debiasing operation matching the phase change range to obtain a second complex sequence; the complex sequence includes the amplitude change and phase change in the distance interval where the target position is located in the slow time dimension;

[0174] The second operation module 40 is used to determine the target phase sequence corresponding to the second complex sequence.

[0175] Through the device provided in this embodiment, the original phase sequence of the signal data at the target position and the phase unwrapping sequence corresponding to the original phase sequence are determined; based on the original phase sequence and the phase unwrapping sequence, the corresponding phase change range is determined; based on a debiasing operation matching the phase change range, the first complex sequence of the signal data is processed to obtain a second complex sequence; the complex sequence includes the amplitude change and phase change in the distance interval where the target position is located in the slow time dimension; the target phase sequence corresponding to the second complex sequence is determined, solving the problem that it is impossible to adapt to all phase change ranges generated by the breathing of different measured targets, resulting in the inability to effectively remove the DC interference, and realizing adapting to all phase change ranges generated by the breathing of different measured targets to effectively remove the DC interference.

[0176] In some of these embodiments, the first operation module 10 is further used to obtain the first complex sequence of the signal data; perform a de-mean operation on the first complex sequence; calculate the phase value of the de-meaned first complex sequence to obtain the original phase sequence of the signal data at the target position.

[0177] In some of these embodiments, the first operation module 10 is further used to process the original phase sequence through a phase unwrapping function to obtain the phase unwrapping sequence corresponding to the original phase sequence.

[0178] In some of these embodiments, the determination module 20 is further used to perform spectral analysis on the band-pass filtered original phase sequence and the phase unwrapping sequence respectively to obtain the first spectrum corresponding to the original phase sequence and the second spectrum corresponding to the phase unwrapping sequence; determine the first maximum amplitude in the first spectrum and the second maximum amplitude in the second spectrum; determine the target sequence in the original phase sequence and the phase unwrapping sequence according to the comparison result of the first maximum amplitude and the second maximum amplitude; determine the corresponding phase change range based on the target sequence.

[0179] In some of these embodiments, the processing module 30 is further configured to compare the phase change range with a preset threshold; and select a debiasing operation that matches the phase change range according to the comparison result.

[0180] In some of these embodiments, the processing module 30 is further configured to process the first complex number sequence after mean removal through a circle fitting algorithm to obtain the corresponding center coordinates; determine the corresponding first DC baseline bias based on the center coordinates; and determine a second complex number sequence based on the first DC baseline bias and the first complex number sequence after mean removal.

[0181] In some of these embodiments, the processing module 30 is further configured to perform low-pass filtering on the original phase sequence to obtain a corresponding low-pass phase sequence; determine a plurality of center coordinates according to a plurality of preset radii, the mean position of the upper boundary points corresponding to the low-pass phase sequence, and the mean position of the lower boundary points; determine the corresponding second DC baseline bias based on the optimal center coordinate among the center coordinates; and determine a second complex number sequence based on the second DC baseline bias and the first complex number sequence after mean removal.

[0182] In some of these embodiments, the processing module 30 is further configured to traverse each center coordinate, and based on each center coordinate, determine a spectral amplitude sequence corresponding to the first complex number sequence after mean removal; determine a corresponding target value based on the spectral amplitude sequence, the maximum value of the spectral amplitude sequence, and the index corresponding to the maximum value; the target value is the proportion of the respiratory harmonic frequency energy to the fundamental frequency energy; and use the center coordinate corresponding to the minimum target value as the optimal center coordinate.

[0183] It should be noted that the above-mentioned various modules can be functional modules or program modules, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned various modules can be located in the same processor; or the above-mentioned various modules can also be located in different processors in any combined form.

[0184] In this embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0185] Optionally, the above computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0186] Optionally, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0187] S1, determining an original phase sequence of signal data at a target position and a phase unwrapping sequence corresponding to the original phase sequence;

[0188] S2, determining the corresponding phase variation range based on the original phase sequence and the phase unwrapping sequence;

[0189] S3, based on a debiasing operation matching the phase variation range, processing the first complex sequence of signal data to obtain a second complex sequence; the complex sequence includes amplitude variation and phase variation of the distance interval where the target position is located in the slow time dimension;

[0190] S4, determining a target phase sequence corresponding to the second complex sequence.

[0191] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0192] In addition, in combination with the DC offset removal method for the vital sign radar signal provided in the above embodiments, a storage medium may also be provided in this embodiment to implement the method. The storage medium stores a computer program; when the computer program is executed by a processor, any DC offset removal method for the vital sign radar signal in the above embodiments is implemented.

[0193] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0194] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

[0195] The term "embodiment" in this application refers to a specific feature, structure or characteristic described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various locations in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is clearly or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0196] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for removing DC bias from a vital sign radar signal, characterized in that, The method includes: Determining an original phase sequence of signal data at a target position and a phase unwrapping sequence corresponding to the original phase sequence; Determining a corresponding phase change range based on the original phase sequence and the phase unwrapping sequence; Processing a first complex sequence of the signal data based on a debiasing operation matching the phase change range to obtain a second complex sequence; the complex sequence includes amplitude changes and phase changes in a distance interval where the target position is located in the slow time dimension; Determining a target phase sequence corresponding to the second complex sequence.

2. The method for removing the DC bias of the vital sign radar signal according to claim 1, wherein The determining of the original phase sequence of the signal data at the target position includes: Obtaining the first complex sequence of the signal data; Performing a de-mean operation on the first complex sequence; Calculating phase values of the de-meaned first complex sequence to obtain the original phase sequence of the signal data at the target position.

3. The method for removing the DC bias of the vital sign radar signal according to claim 1, characterized in that Determining the phase unwrapping sequence corresponding to the original phase sequence includes: Processing the original phase sequence through a phase unwrapping function to obtain the phase unwrapping sequence corresponding to the original phase sequence.

4. The method for removing the DC bias of the vital sign radar signal according to claim 1, wherein The determining of the corresponding phase change range based on the original phase sequence and the phase unwrapping sequence includes: Performing spectral analysis on the band-pass filtered original phase sequence and the phase unwrapping sequence respectively to obtain a first spectrum corresponding to the original phase sequence and a second spectrum corresponding to the phase unwrapping sequence; Determining a first maximum amplitude in the first spectrum and a second maximum amplitude in the second spectrum; Determining a target sequence in the original phase sequence and the phase unwrapping sequence according to a comparison result between the first maximum amplitude and the second maximum amplitude; Determining the corresponding phase change range based on the target sequence.

5. The method for removing the DC bias of the vital sign radar signal according to claim 1, characterized in that, Selecting a debiasing operation matching the phase change range includes: Comparing the phase change range with a preset threshold; Selecting the debiasing operation matching the phase change range according to the comparison result.

6. The method for removing the DC bias of the vital sign radar signal according to claim 5, characterized in that, When the phase change range is greater than the preset threshold, the processing of the first complex sequence of the signal data based on the debiasing operation matching the phase change range to obtain a second complex sequence includes: Processing the de-meaned first complex sequence through a circle fitting algorithm to obtain corresponding center coordinates; Determining a corresponding first DC baseline bias based on the center coordinates; Determining the second complex sequence based on the first DC baseline bias and the de-meaned first complex sequence.

7. The method for removing the DC bias of the vital sign radar signal according to claim 5, characterized in that When the phase change range is less than or equal to the preset threshold, the processing of the first complex sequence of the signal data based on the debiasing operation matching the phase change range to obtain a second complex sequence includes: Performing low-pass filtering on the original phase sequence to obtain a corresponding low-pass phase sequence; Determining a plurality of center coordinates according to a plurality of preset radii, an upper boundary point mean position and a lower boundary point mean position corresponding to the low-pass phase sequence; Determine a corresponding second DC baseline offset based on the optimal center coordinate among each of the center coordinates; Determine the second complex sequence based on the second DC baseline offset and the first complex sequence after mean removal.

8. The method for removing the DC bias of the vital sign radar signal according to claim 7, wherein, Determining the optimal center coordinate among each of the center coordinates includes: Traverse each of the center coordinates, and based on each center coordinate, determine a corresponding spectral amplitude sequence for the first complex sequence after mean removal; Based on the spectral amplitude sequence, the maximum value of the spectral amplitude sequence, and the index corresponding to the maximum value, determine a corresponding target value; the target value is the proportion of the respiratory harmonic frequency energy to the fundamental frequency energy; Take the center coordinate corresponding to the minimum target value as the optimal center coordinate.

9. A DC bias removal device for a vital sign radar signal, characterized in that, The device includes: a first operation module, a determination module, a processing module, and a second operation module; The first operation module is configured to determine an original phase sequence of signal data at a target position and a phase unwrapping sequence corresponding to the original phase sequence; The determination module is configured to determine a corresponding phase change range based on the original phase sequence and the phase unwrapping sequence; The processing module is configured to process the first complex sequence of the signal data based on a debiasing operation matching the phase change range to obtain a second complex sequence; the complex sequence includes the amplitude change and phase change of the distance interval where the target position is located in the slow time dimension; The second operation module is configured to determine a target phase sequence corresponding to the second complex sequence.

10. A computer device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the method for removing DC bias of the vital sign radar signal according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method for removing DC bias of the vital sign radar signal according to any one of claims 1 to 8 are implemented.

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