A method and system for suppressing respiratory harmonics in UWB impact bioradar heartbeat signals

Abstract

The present invention discloses a method and system for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal, and belongs to the field of human heartbeat signal detection. The method comprises: obtaining the position of respiratory harmonics based on the fundamental frequency of respiration, judging whether the respiratory harmonics and the main peak of the heartbeat coincide with each other through a correlation analysis method, and then determining the respiratory harmonics to be removed; and suppressing each harmonic of respiration using a feedback notch filter optimized by a genetic algorithm. The method causes little loss to the radar heartbeat signal and can automatically remove each harmonic, thereby providing a more refined radar heartbeat signal for extracting cardiac motion features. The method effectively improves notch performance in terms of bandwidth, overshoot, etc., considers the processing method for the coincidence of the respiratory harmonics and the main peak frequency of the heartbeat, and retains the heartbeat frequency component to the greatest extent, thereby providing a new method for radar heartbeat signal extraction and analysis.

Description

Technical Field

[0001] The invention belongs to the field of human heartbeat signal detection and relates to a method and system for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal. Background Art

[0002] Heartbeat is one of the most important vital signs. Monitoring heartbeats can reveal heart health and, in turn, assess human health. For example, changes in heart rate variability and the interval between cardiac contractions can be used to monitor and diagnose heart disease. Therefore, accurate heartbeat signal detection and feature extraction are crucial.

[0003] Electrocardiography and photoplethysmography are two commonly used contact methods for measuring heartbeat signals. However, these methods have certain drawbacks. For example, the test site must be cleaned and electrodes connected beforehand; and contact methods are not suitable for those who are allergic to the electrodes. With the development and integration of radar technology and biomedical engineering, bioradar technology has gradually emerged. This technology can detect physiological signals such as heartbeat and respiration without contact and at a distance, penetrating obstacles such as wood and clothing. It is suitable for detecting heartbeat signals in patients with highly contagious diseases and severe burns, as well as for diagnosing heart disease.

[0004] Typically, radar heartbeat signals after preprocessing (bandpass filtering) contain respiratory harmonics, which interfere with the heartbeat signal. The position and magnitude of respiratory harmonics in radar heartbeat signals vary, hindering their separation. Research on respiratory harmonic removal is limited. Some papers have used signal decomposition-based methods to remove respiratory harmonics, achieving some success. However, these methods employ a band-stop approach to remove a frequency band containing a respiratory harmonic. This process results in excessive loss of radar heartbeat information and cannot automatically select the frequency band to be discarded. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem in the prior art that too much radar heartbeat information is lost when removing a frequency band containing a certain respiratory harmonic, and the selection of the frequency band to be discarded cannot be completed automatically. A method and system for suppressing respiratory harmonics in the heartbeat signal of a UWB impact bioradar is provided.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention proposes a method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal, comprising the following steps:

[0008] Obtain data after high-frequency noise removal based on radar echo data after DC and baseline drift removal, and obtain a range unit with the largest slow-time signal accumulation amount among all range units based on the data after high-frequency noise removal;

[0009] The radar respiration signal is obtained according to the distance unit with the largest slow time signal accumulation among all the distance units, and the respiratory harmonic signal mixed in the heartbeat is obtained according to the radar respiration signal;

[0010] The signal correlation analysis results are obtained based on the respiratory harmonic signal mixed in the heartbeat and the multiple frequency signals of the respiratory fundamental frequency. The signal correlation analysis results are processed to obtain the radar heartbeat signal after removing the respiratory harmonics, thereby realizing respiratory harmonic suppression.

[0011] Preferably, radar echo data R after removing DC and baseline drift is obtained. DC The (m,n) method is as follows:

[0012]

[0013] Where Raw(m,n) is the unprocessed radar echo data, m and n represent the number of fast-time and slow-time samples, respectively. m represents the number of rows of radar echo data, and n represents the number of columns of radar echo data (1≤m≤M, 1≤n≤N). M represents the total number of fast-time samples, and N represents the total number of slow-time samples. The value of x ranges from 1 to (N-99).

[0014] Preferably, the data R after removing high-frequency noise is obtained. LP The (m,n) method is as follows:

[0015] R LP (m,n)=R DC (m,n)*H LP1 (t) (2)

[0016] Among them, H LP1 (t) is the finite impulse response function of the low-pass filter, R DC (m,n) is the radar echo data after removing DC and baseline drift;

[0017] The distance unit m1 with the largest slow-time signal accumulation among all distance units is obtained as follows:

[0018]

[0019] Where S(m) is the accumulated amount of slow-time signal on the m-th range unit, and m1 is the number of the range unit with the largest accumulated amount.

[0020] Preferably, the radar breathing signal R is obtained TP (n) as follows:

[0021] R TP (n) = R LP (m1,n)*H LP2 (t) (4)

[0022] Among them, H LP2 (t) is the finite impulse response function of the low-pass filter, R LP (m1,n) is the slow-time signal at the point where the accumulated amount of slow-time signal is the largest among all distance units;

[0023] The method for obtaining the respiratory harmonic frequency f(k) is as follows:

[0024]

[0025] Among them, F TP (f) is the frequency domain respiratory signal after Fourier transform, F is the Fourier transform symbol, maxvalue is the maximum value solution function, f r is the fundamental frequency of breathing, F r is the amplitude corresponding to the fundamental frequency of breathing, f(k) is k times of the fundamental frequency of breathing, k is an integer, and the value is 3, 4, or 5.

[0026] Preferably, the method for obtaining the respiratory harmonic signal mixed in the heartbeat is as follows:

[0027] R Hf(k) (n) = R HP (n)*H BPk (t) (6)

[0028] Among them, H BPk (t) is the finite impulse response function of the bandpass filter for the kth harmonic, R HP (n) is the radar heartbeat signal containing respiratory harmonics;

[0029] Get radar heartbeat signal R HP (n) as follows:

[0030] R HP (n) = R LP (m1,n)*H BP (t) (7)

[0031] Among them, H BP (t) is the finite impulse response function of the bandpass filter, R LP (m1,n) is the slow-time signal at the point where the accumulated amount of slow-time signal is the largest among all distance units.

[0032] Preferably, the method for obtaining the signal correlation analysis result is as follows:

[0033]

[0034] Where N is the length of the signal, R Hf(k) (n) is the respiratory harmonic signal mixed in the heartbeat, is the average value of the respiratory harmonic signal mixed in the heartbeat, R TPk (n) is the frequency multiplication signal obtained by interpolation of the respiratory fundamental frequency signal, and the obtained respiratory fundamental frequency signal R TP (n) Perform k-fold frequency interpolation to obtain the k-fold frequency time domain signal R of the radar breathing signal TPk (n), is the average value of the k-fold frequency time domain signal obtained by interpolating the respiratory fundamental frequency signal, p k is the obtained kth harmonic correlation coefficient.

[0035] Preferably, the method for obtaining the radar heartbeat signal after removing respiratory harmonics is as follows:

[0036] Signal correlation analysis results p k >K p When , the kth respiratory harmonic and the main peak of the heartbeat do not coincide;

[0037] Signal correlation analysis results p k ≤K p When , the kth respiratory harmonic coincides with the main peak of the heartbeat;

[0038] When the kth respiration harmonic coincides with the main peak of the heartbeat, the coincident harmonic frequency is retained as the radar heartbeat signal;

[0039] When the kth respiratory harmonic does not coincide with the main peak of the heartbeat, the respiratory harmonic is removed to obtain the radar heartbeat signal after removing the respiratory harmonic interference;

[0040] Among them, the threshold K is set p The settings are obtained by contact devices.

[0041] The present invention proposes a system for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal, comprising:

[0042] A signal preprocessing module is used to obtain data after high-frequency noise is removed from the radar echo data after DC and baseline drift are removed, and to obtain a range unit with the largest slow-time signal accumulation amount among all range units based on the data after high-frequency noise is removed;

[0043] a respiratory harmonic frequency acquisition module, wherein the respiratory harmonic frequency acquisition module is used to acquire a radar respiratory signal according to the distance unit with the largest slow time signal accumulation amount among all distance units, and to acquire a respiratory harmonic signal mixed in the heartbeat according to the radar respiratory signal;

[0044] The radar heartbeat signal acquisition module is used to obtain signal correlation analysis results based on the respiratory harmonic signal and the multiple frequency signals of the respiratory fundamental frequency mixed in the heartbeat, process the signal correlation analysis results to obtain the radar heartbeat signal after removing the respiratory harmonics, and realize respiratory harmonic suppression.

[0045] A computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the method for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal is implemented.

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention proposes a method for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal. Respiratory harmonics interfere with the heartbeat signal, resulting in inaccurate heartbeat signal detection, which is a difficulty in this field. The existing technology removes harmonics by using two traditional non-feedback notch filters. This method has a wide bandwidth, large overshoot, and does not consider the processing problem when the respiratory harmonics and the main peak frequency of the heartbeat coincide, resulting in the loss of useful frequency components of the heartbeat, affecting the extraction and feature analysis of the radar heartbeat signal, and further affecting the application of radar non-contact heartbeat signal extraction and disease diagnosis. The present invention is based on a point-frequency respiratory harmonic removal method. Before each harmonic removal, the optimal structural parameters of the filter are found through an intelligent optimization algorithm, ensuring the maximum removal of respiratory harmonics while minimizing the loss of the heartbeat signal. At the same time, the present invention removes respiratory harmonics based on the respiratory fundamental frequency, and can automatically complete the positioning and removal of respiratory harmonics. In addition, this method considers the processing problem of respiratory harmonics when the respiratory harmonics coincide with the main peak of the radar heartbeat signal. The respiratory harmonic suppression method based on parameter optimization effectively improves the notch performance in terms of bandwidth, overshoot, etc., takes into account the processing method for the coincidence of respiratory harmonics and the main peak frequency of the heartbeat, retains the heartbeat frequency component to the greatest extent, and provides a new method for radar heartbeat signal extraction and analysis.

[0049] This paper proposes a system for suppressing respiratory harmonics in UWB impact bio-radar heartbeat signals. This system achieves respiratory harmonic suppression by dividing the system into a signal preprocessing module, a respiratory harmonic frequency acquisition module, and a radar heartbeat signal acquisition module. The modular design makes each module independent, facilitating unified management of the modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 This is a flow chart of the method for suppressing respiratory harmonics in the heartbeat signal of the UWB impact bioradar of the present invention.

[0052] Figure 2 This is a flow chart of the harmonic suppression method based on parameter optimization of the present invention.

[0053] Figure 3 Schematic diagram of the DC offset removal effect of the present invention ((a) echo signal before DC offset removal; (b) echo signal after 100-step DC offset removal).

[0054] Figure 4 These are the signals before and after high-frequency interference removal according to the present invention ((a) echo signal before high-frequency interference removal; (b) echo signal after interference removal).

[0055] Figure 5 This is the present invention ((a) a two-dimensional matrix composed of slow time and fast time, (b) is a square line graph, (c) is the horizontal axis slow time and the vertical axis is the sum of the squares corresponding to the slow time. It can be seen that the energy is maximum at the position corresponding to the 7th distance unit).

[0056] Figure 6 The radar breathing signal and radar heartbeat signal obtained by processing according to the present invention ((a) is the radar breathing signal, (b) is the radar heartbeat signal).

[0057] Figure 7 Flow chart of the genetic method of the present invention.

[0058] Figure 8 The fitness curves of the five notch frequency points optimized by the genetic algorithm of the present invention ((a) fitness curve of notch frequency 0.85 Hz; (b) fitness curve of notch frequency 1.46 Hz; (c) fitness curve of notch frequency 2.08 Hz; (d) fitness curve of notch frequency 2.69 Hz; (e) fitness curve of notch frequency 3.3 Hz).

[0059] Figure 9 The target planes and optimal coordinates of the five notch frequency points optimized by the genetic algorithm of the present invention ((a) the optimal plane and optimal point coordinates when the notch frequency is 0.85 Hz; (b) the optimal plane and optimal point coordinates when the notch frequency is 1.46 Hz; (c) the optimal plane and optimal point coordinates when the notch frequency is 2.08 Hz; (d) the optimal plane and optimal point coordinates when the notch frequency is 2.69 Hz; (e) the optimal plane and optimal point coordinates when the notch frequency is 3.3 Hz).

[0060] Figure 10 This is the result of genetic algorithm parameter optimization of the present invention.

[0061] Figure 11The optimal structural parameters and amplitude-frequency characteristic curves of the feedback trap at the respiratory harmonic frequency point are obtained by parameter optimization of the present invention ((a) the optimal plane and optimal point coordinates corresponding to 0.896 Hz; (b) the amplitude-frequency characteristic curve when the trap frequency point is 0.896 Hz; (c) the optimal plane and optimal point coordinates corresponding to 1.195 Hz; (d) the amplitude-frequency characteristic curve when the trap frequency point is 1.195 Hz; (e) the optimal plane and optimal point coordinates corresponding to 1.494 Hz; (f) the amplitude-frequency characteristic curve when the trap frequency point is 1.494 Hz).

[0062] Figure 12 These are the radar heartbeat signals before and after the respiratory harmonics are removed.

[0063] Figure 13 This is a diagram of the respiratory harmonic suppression system in the UWB impact bio-radar heartbeat signal of the present invention. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0066] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0067] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0068] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0069] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0070] The present invention is described in further detail below with reference to the accompanying drawings:

[0071] Referring to the figure, the present invention proposes a method for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal, such as Figure 1 As shown, the following steps are included:

[0072] S1. Obtain data after removing high-frequency noise from radar echo data after removing DC and baseline drift, and obtain the range unit with the largest slow-time signal accumulation amount among all range units based on the data after removing high-frequency noise;

[0073] Get radar echo data R after removing DC and baseline drift DC The (m,n) method is as follows:

[0074]

[0075] Wherein, Raw(m,n) is the unprocessed radar echo data, m and n represent the number of fast-time and slow-time samples, respectively. m represents the number of rows of radar echo data, and n represents the number of columns of radar echo data (1≤m≤M, 1≤n≤N). The value of x ranges from 1 to (N-99), M represents the total number of fast-time samples, and N represents the total number of slow-time samples.

[0076] Get the data R after removing high-frequency noise LP The (m,n) method is as follows:

[0077] R LP (m,n)=R DC (m,n)*H LP1 (t) (2)

[0078] Among them, H LP1 (t) is the finite impulse response function of the low-pass filter, R DC(m,n) is the radar echo data after removing DC and baseline drift.

[0079] The distance unit m1 with the largest slow-time signal accumulation among all distance units is obtained as follows:

[0080]

[0081] Where S(m) is the accumulated amount of slow-time signal on the m-th range unit, and m1 is the number of the range unit with the largest accumulated amount.

[0082] S2. Obtain a radar breathing signal based on the distance unit with the largest slow-time signal accumulation among all distance units, and obtain a breathing harmonic frequency based on the radar breathing signal;

[0083] Get radar breathing signal R TP (n) as follows:

[0084] R TP (n) = R LP (m1,n)*H LP2 (t) (4)

[0085] Among them, H LP2 (t) is the finite impulse response function of the low-pass filter, R LP (m1,n) is the slow-time signal at the point where the accumulated amount of slow-time signal is the largest among all distance units;

[0086] The method for obtaining the respiratory harmonic frequency f(k) is as follows:

[0087]

[0088] Among them, F TP (f) is the frequency domain respiratory signal after Fourier transform, F is the Fourier transform symbol, maxvalue is the maximum value solution function, f r is the fundamental frequency of breathing, F r is the amplitude corresponding to the fundamental frequency of breathing, f(k) is k times the fundamental frequency of breathing, k is an integer, and is generally taken as 3, 4, or 5 according to the characteristics of the human respiratory frequency and heart rate band.

[0089] The obtained respiratory fundamental frequency signal R TP (n) Perform k-fold frequency interpolation to obtain the k-fold frequency time domain signal R of the radar breathing signal TPk (n).

[0090] S3. Obtain signal correlation analysis results based on the respiratory harmonic frequency and the multiple frequency signals of the respiratory fundamental frequency, process the signal correlation analysis results to obtain the radar heartbeat signal after removing the respiratory harmonics, and achieve respiratory harmonic suppression.

[0091] Based on the multiple values f(k) of the respiratory fundamental frequency, a bandpass filter is used to filter out the time domain signals at the respiratory harmonic frequency points within the heartbeat signal band, that is, the respiratory harmonic signals mixed in the heartbeat signal. It is determined whether these respiratory harmonic signals coincide with the main peak of the heartbeat frequency. If so, the signal is retained; otherwise, it is regarded as a pure respiratory harmonic interference component and is removed.

[0092] The method for obtaining the respiratory harmonic signal mixed in the heartbeat is as follows:

[0093] R Hf(k) (n) = R HP (n)*H BPk (t) (6)

[0094] Among them, H BPk (t) is the finite impulse response function of the bandpass filter for the kth harmonic, R HP (n) is the radar heartbeat signal containing respiratory harmonics;

[0095] Get radar heartbeat signal R HP (n) as follows:

[0096] R HP (n) = R LP (m1,n)*H BP (t) (7)

[0097] Among them, H BP (t) is the finite impulse response function of the bandpass filter, R LP (m1,n) is the slow-time signal at the point where the accumulated amount of slow-time signal is the largest among all distance units.

[0098] The method for obtaining signal correlation analysis results is as follows:

[0099]

[0100] Where N is the length of the signal, R Hf(k) (n) is the respiratory harmonic signal mixed in the heartbeat, is the average value of the respiratory harmonic signal mixed in the heartbeat, R TPk (n) is the frequency multiplication signal obtained by interpolation of the respiratory fundamental frequency signal, and the obtained respiratory fundamental frequency signal R TP (n) Perform k-fold frequency interpolation to obtain the k-fold frequency time domain signal R of the radar breathing signal TPk (n), is the average value of the k-fold frequency time domain signal obtained by interpolating the respiratory fundamental frequency signal, p k is the obtained kth harmonic correlation coefficient.

[0101] The method for obtaining the radar heartbeat signal after removing respiratory harmonics is as follows:

[0102] Signal correlation analysis results p k >K p When , the kth respiratory harmonic and the main peak of the heartbeat do not coincide;

[0103] Signal correlation analysis results p k ≤K p When , the kth respiratory harmonic coincides with the main peak of the heartbeat;

[0104] When the kth respiration harmonic coincides with the main peak of the heartbeat, the coincident harmonic frequency is retained as the radar heartbeat signal;

[0105] When the kth respiratory harmonic does not coincide with the main heartbeat peak, the respiratory harmonic is removed to obtain a radar heartbeat signal after removing the respiratory harmonic interference;

[0106] Among them, the threshold K is set p The settings are obtained by contact devices.

[0107] The detailed process of harmonic suppression method based on parameter optimization is as follows Figure 2 As shown in the figure, first, the harmonic frequencies of the respiratory signal are obtained based on the respiratory fundamental frequency signal obtained by low-pass. Secondly, the correlation analysis method is used to determine whether the heartbeat frequency coincides with the respiratory harmonics mixed in the heartbeat. If they coincide, the overlapping frequency components are retained as the radar heartbeat signal. If they do not coincide, the respiratory harmonics are removed. Finally, the radar heartbeat signal is obtained after the respiratory harmonic interference is removed.

[0108] Step 1: Static clutter removal based on the 100th-order DC offset removal method

[0109] The radar echo signal is a matrix with m rows and n columns, as shown in formula (1), denoted as R[m,n].

[0110] Raw(m,n)=r(t=mT f ,τ=nT s ) (1)

[0111] m and n represent the number of samples of fast time and slow time respectively. f is the fast time sampling interval, T s is the slow-time pulse duration. The row vector records the signals received at different observation times within each range interval, and the column vector records the signals received at different range intervals at each time point. A two-dimensional matrix contains both time and distance information in the radar echo signal. Cardiac physiological movement, or heartbeat signals, are converted by the radar system into heartbeat signals within the radar echo signal, referred to below as radar heartbeat signals.

[0112] The original radar echo signal contains DC components caused by static objects such as tables and floors, as well as baseline drift caused by environmental factors. This interference will cause strong interference to the radar heartbeat signal. The DC offset removal method is used to remove the DC component and baseline drift. Figure 3 As shown in (a) and (b) of Figure 1. After using 80-order DC offset removal, the signal is fuzzy in the initial part; after using 100-order processing, the overall signal is clearer than the 80-order processing result; after using 120-order processing, the signal processing result is not significantly improved. As the order increases, the amount of calculation will increase, so this article uses the 100-order DC offset removal method. This step is described by the following formula (2):

[0113]

[0114] Raw(m,n) is the unprocessed radar echo data. m and n represent the fast time and slow time sampling indices (1≤m≤M, 1≤n≤N), respectively. m represents the number of rows of radar echo data, and n represents the number of columns of radar echo data. The value of x ranges from 1 to (N-99).

[0115] Step 2: Remove high-frequency interference from environmental noise based on low-pass filtering

[0116] The echo signal contains high-frequency noise interference such as environmental noise. This paper uses a low-pass filter to filter out high-frequency noise interference to obtain a mixed signal of breathing and heartbeat. First, the cutoff frequency selection of the low-pass filter is analyzed. If the cutoff frequency is lower than the normal human heartbeat frequency range, it will affect the radar heartbeat signal separation effect in the next step. If the cutoff frequency is too high, the high-frequency interference removal will not be obvious. For this purpose, multiple cutoff frequency tests were conducted. When the cutoff frequency is 5Hz, the noise removal effect is optimal. This step is described by the following formula (3). The results before and after processing are as follows: Figure 4 As shown in (a) and (b).

[0117] R LP (m,n)=R DC (m,n)*H LP1 (t) (3)

[0118] R LP (m,n) is the data after removing high-frequency noise, H LP1 (t) is the finite impulse response function of the low-pass filter.

[0119] Step 3: Human target positioning based on maximum sum of squares

[0120] After preprocessing, environmental interference has been removed. The characteristics of pulse ultra-wideband radar make echo signals appear within multiple distance units. In order to separate the radar heartbeat signal, it is first necessary to select and locate the best distance unit, that is, the location of the human body. The entire slow-time signal value in each distance unit is squared and then summed. The distance unit with the largest square sum is selected as the location of the human target. The result is as follows: Figure 5 As shown, this step is described by formula (4). The results of the entire distance range and the entire time range of the detection are as follows Figure 5 As shown in (a), the row number in the figure represents the detection distance, and the column number represents the detection duration; the square sum of the data in each row is calculated, and the result is as follows Figure 5 As shown in (b), the sum of squares is the largest at the 7th distance unit. The sum of squares data is plotted, and the plot result is as follows: Figure 5 As shown in (c), the peak represents the position of the human target, which is the optimal distance unit.

[0121]

[0122] R LP (m,n) is the slow-time signal in a certain distance unit after removing high-frequency noise, S(m) is the cumulative amount (energy) of the slow-time signal on the m-th distance unit, and m1 is the number of the distance unit with the largest cumulative amount.

[0123] Step 4: Heartbeat separation based on bandpass filtering

[0124] After the above processing, the signal in the optimal distance cell is a mixed signal of breathing and heartbeat, and the heartbeat signal and the breathing signal can be separated by filtering. To obtain the radar heartbeat signal, a bandpass filter is used to separate the breathing and heartbeat signals from the radar echo signal. The lower limit of a normal human heart rate is 60 beats per minute, which is converted to a frequency of 1Hz. However, some professional athletes are physically strong and have relatively good heart function. Their hearts pump a lot of blood each time, and a fewer number of beats can still meet their physical needs, with only more than 50 beats per minute. This paper uses 51 beats, which is converted to a frequency of 0.85Hz. For this reason, the lower cutoff frequency of the bandpass filter is set to 0.85Hz. The calculation formula for the maximum heartbeat rate of a person after exercise is 220-age. The present invention uses the maximum heartbeat rate of 200 at the age of 20, which is converted to a heart rate of 3.3Hz. For this reason, the upper cutoff frequency of the bandpass filter is set to 3.3Hz. A normal adult breathes at least 16 times per minute, and a newborn breathes 44 times per minute. Taking the middle value of 30, it is converted to a cutoff frequency of 0.5Hz. Therefore, a low-pass filter with a cutoff frequency of 0.5Hz is used to obtain the radar respiratory signal. The low-pass filter processing result is as follows Figure 6 As shown in (a), the bandpass processing result is as follows Figure 6 As shown in (b), this step is described by formulas (5) and (6):

[0125] R HP (n) = R LP (m1,n)*H BP (t) (5)

[0126] R TP (n) = R LP (m1,n)*H LP2 (t) (6)

[0127] R HP (n) is the radar heartbeat signal obtained, H BP (t) is the finite impulse response function of the bandpass filter, R TP (n) is the obtained radar breathing signal, H LP2 (t) is the finite impulse response function of the low-pass filter. R LP (m1, n) is the slow-time signal at the point where the cumulative amount of slow-time signals in all distance units is the largest. The slow-time signal is passed through a band-pass filter to obtain the radar heartbeat signal, and is passed through a low-pass filter to obtain the radar breathing signal.

[0128] The obtained respiratory fundamental frequency signal R TP (n) Perform k-fold frequency interpolation to obtain the k-fold frequency time domain signal R of the radar breathing signal TPk (n).

[0129] Step 5: Respiratory harmonic positioning

[0130] The data obtained after the rough separation of the radar heartbeat signal contains not only heartbeat information but also respiratory harmonic information. Respiratory harmonics interfere with the heartbeat, so it is necessary to remove the respiratory harmonic signal and retain the heartbeat signal component as much as possible.

[0131] The harmonic signal frequency is an integer multiple of the fundamental signal frequency. In the previous article, the respiratory signal was obtained through low-pass filtering. The respiratory time-domain signal was Fourier transformed to obtain the respiratory frequency-domain signal. Matlab's built-in function was used to obtain the coordinates of the maximum value of the frequency-domain signal. The horizontal coordinate of the maximum value point is the fundamental frequency of the respiratory signal. The 3rd, 4th, and 5th harmonics of the respiratory signal are most likely to fall within the radar heartbeat signal frequency band, and the 3rd, 4th, and 5th harmonics account for the majority of all harmonics. Multiplying the fundamental frequency of the respiratory signal by the integers 3, 4, and 5 yields the frequency values of each harmonic. Finding the corresponding frequency point within the radar heartbeat signal frequency band also locates the location of the respiratory harmonic. The calculation of the respiratory harmonic f(k) is described by formula (7), and the harmonic location results are shown in Table 1. This completes the location of the respiratory harmonics.

[0132]

[0133] Among them, F TP(f) is the frequency domain respiratory signal after Fourier transform, F is the Fourier transform symbol, maxvalue is the maximum value solution function, f r is the fundamental frequency of breathing, F r is the amplitude corresponding to the fundamental frequency of breathing, f(k) is k times the fundamental frequency of breathing, k is an integer. According to the characteristics of the human respiratory frequency and heartbeat frequency band, it is generally taken as 3, 4, or 5. That is, the 3rd, 4th, and 5th respiratory harmonics of ordinary people are often mixed into the frequency band of the heartbeat signal.

[0134] Table 1 Harmonic positioning results

[0135]

[0136] According to the multiple frequency values f(k) of the respiratory fundamental frequency, a bandpass filter is used to filter out the time domain signals at the positions of the respiratory harmonics within the heartbeat signal frequency band, that is, the respiratory harmonic signals mixed in the heartbeat signal.

[0137] R Hf(k) (n) = R HP (n)*H BPk (t) (8)

[0138] Among them, H BPk (t) is the finite impulse response function of the bandpass filter for the kth harmonic, R HP (n) is the heartbeat signal containing respiratory harmonics, R Hf(k) (n) is the kth respiratory harmonic signal filtered out from the heartbeat signal, and k is generally taken as 3, 4, or 5.

[0139] Step 6: Determine whether the harmonics and heartbeats coincide

[0140] The present invention uses correlation analysis to determine whether respiratory harmonics and radar heartbeat signals coincide. One channel of data for the correlation analysis is the respiratory fundamental frequency signal obtained by low-pass, and the other channel is the respiratory harmonic signal obtained by respiratory harmonic positioning. However, the correlation calculation requires interpolating the respiratory fundamental frequency to the frequency corresponding to the respiratory harmonic. The Pearson correlation coefficient calculation formula is shown in formula (9):

[0141]

[0142] It is R Hf(k) (n) and R TPk covariance of (n), It is R Hf(k) The standard deviation of (n), It is R TPk (n), where N is the length of the signal.

[0143] R Hf(k)(n) is the respiratory harmonic signal mixed in the heartbeat, R TPk (n) represents the frequency-multiplied signals obtained by interpolating the respiratory fundamental frequency signal. When the correlation coefficient is between 0.8 and 1, the two vectors are highly correlated; between 0.6 and 0.8, they are strongly correlated; between 0.4 and 0.6, they are moderately correlated; between 0.2 and 0.4, they are weakly correlated; between 0 and 0.2, they are extremely weakly correlated; and when the correlation coefficient is 0, the two vectors are uncorrelated.

[0144] The results of correlation analysis were p k , through p k The value of the value determines whether the main peak of the respiratory harmonic signal and the radar heartbeat signal coincides. The threshold K is set p To judge the degree of signal overlap, when p k >K p When the respiratory harmonics and the main peak of the heartbeat do not coincide, when p k ≤K p When the respiratory harmonics and the heartbeat main peak coincide, the coincident harmonics are retained. Otherwise, the coincident harmonics are not retained and all three harmonic signals are removed. p The value is 0.5, K p The value is set by the contact device and K can be adjusted in time according to the actual detection situation. p The size of the normal breathing signal of a person lying down for 45 seconds is used for correlation analysis. The results are shown in Table 2:

[0145] Table 2 Heartbeat and harmonic correlation analysis results

[0146]

[0147] The table shows that the correlation coefficient between the respiratory fundamental frequency signal and the third, fourth, and fifth harmonics of respiration is greater than 0.5. The harmonics are very similar to the fundamental frequency signal, indicating that the third, fourth, and fifth harmonics are the primary components of the signal. In most cases, the third, fourth, and fifth harmonics of respiration do not overlap with the main peak of the radar heartbeat signal, allowing for direct respiration harmonic removal.

[0148] Step 7: Parameter-optimized feedback notch removal of respiratory harmonics

[0149] Intelligent optimization algorithm notch parameter optimization

[0150] To achieve the best notch effect (i.e., narrow bandwidth, deep notch depth, and minimal notch overshoot) at the notch frequency, according to the analysis above, the structural parameters ρ and a of the notch filter need to be continuously adjusted as the notch frequency changes. Therefore, in order to obtain the optimal notch structural parameters ρ and a for each notch frequency, it is necessary to optimize these parameters. Manual search for the optimal structural parameters of the notch filter would be a tremendous amount of work. Optimization methods include traditional optimization methods and intelligent optimization methods. Traditional optimization methods require the optimization object to be differentiable and continuous, while intelligent optimization methods do not require these requirements. Furthermore, intelligent optimization methods operate in a coded manner, so this paper uses intelligent optimization methods for parameter optimization. Existing technologies use genetic and gray wolf algorithms to optimize PID parameters. This invention uses genetic methods to optimize notch parameters.

[0151] The optimization method revolves around the optimization problem, and constructing the optimization problem is equivalent to constructing the objective function. This method uses the algebraic sum, weighted sum, weighted square sum, and normalization of notch depth, notch width, and notch overshoot as the objective function. The objective function parameters are optimized. The optimization results show that while each objective function yields the same optimal parameters, the objective function constructed from the algebraic sum of the three methods minimizes computational complexity. Therefore, this algebraic sum of the three methods is selected as the objective function.

[0152] Optimization of notch parameters based on genetic algorithm

[0153] The inspiration for the genetic optimization method comes from the biological evolution process. This method uses a computer to simulate the replication, crossover, and mutation processes in the evolution process to select the optimal solution in the objective function solution space, which is equivalent to finding the individual in the group that is most suitable for the environment in biological evolution. The flowchart of the genetic algorithm is as follows Figure 7 As shown, the steps are as follows.

[0154] Step 1: Create an initial population and randomly initialize the initial crossover rate, mutation rate, and number of generations.

[0155] Step 2: Calculation of fitness value.

[0156] Step 3: Determine whether the fitness value at this time has reached the optimal value or the maximum number of iterations. If so, go to step 5; otherwise, go to step 4.

[0157] Step 4: Perform selection, crossover and mutation operations on the current population to create a new generation of population.

[0158] Step 5: Output the result at this time.

[0159] The genetic algorithm parameters are set as follows: the range of one parameter ρ of the feedback notch to be optimized by this method is 0 to 1, the range of another parameter α is 0 to 10, the crossover ratio is 0.8, the maximum number of iterations is 200, the population size is 20, the mutation probability is 0.2, and the penalty factor is 100.

[0160] The heartbeat bandpass filter frequency range is divided into five parts and six frequency points. The optimal feedback notch parameters found using a genetic algorithm are shown in Table 3. The average optimization time for each frequency point is 76 seconds. ρ and a represent two parameters of the feedback notch filter, respectively. The total index is the sum of the notch filter's overshoot and the value 1, the absolute value of the difference between the notch depth and the value 1, and the bandwidth. Figure 8 、 Figure 9 and Figure 10 In the figure are the fitness curve, target plane, coordinates of optimal parameters, and statistical results of each frequency point index and . Figure 8 Taking (a) as an example, the figure reflects the process of fitness value changing with the number of iterations. The horizontal axis is the number of iterations of the optimization algorithm. It can be seen that the iteration stops when the stopping condition is reached. The vertical axis is the fitness value, which is the sum of the indicators composed of notch overshoot, notch depth, and notch width. It can be seen that the sum of the indicators is constantly converging. When the iteration stops, the convergence also stops. The other sub-graphs are shown in (a). Figure 9 Taking (a) in the figure as an example, the figure reflects the process of the optimization algorithm searching for the optimal structural parameters ρ and a. The x-axis is the value range of the notch parameter ρ, the y-axis is the range of the notch parameter a, and the z-axis is the sum of the indicators obtained by bringing the notch parameters into the notch filter, that is, the value of the objective function. After the optimization is completed, the entire optimization process is drawn to obtain a surface. The coordinates marked in the figure represent the values of the notch parameters ρ and a when the notch filter parameters are optimal and the objective function value at this time. The other sub-figures are shown in (a). Figure 10 The horizontal axis represents the frequency point addressed by the notch filter, i.e., the notch frequency, while the vertical axis represents the notch parameters ρ and a, as well as the sum of the indices. As can be seen from the three figures, the genetic method finds the optimal parameter values. The optimal ρ and a values for each notch frequency point are different and irregular. The notch parameters also follow no regular pattern as the notch frequency increases. Therefore, it is essential to use optimization methods to find a set of optimal solutions for ρ and a within their respective ranges.

[0161] Table 3 Results of genetic algorithm optimization for five notch frequency points

[0162]

[0163] Feedback notch removes harmonics:

[0164] The results of parameter optimization for the radar echo data of 45 seconds of normal breathing are as follows: Figure 11As shown in the figure, the fundamental frequency of the respiratory signal is 0.299Hz, and the 3rd, 4th, and 5th harmonics of the respiratory signal are 0.896Hz, 1.195Hz, and 1.494Hz respectively. Figure 11 Taking (a) and (b) as examples, (a) is the process of finding the optimal notch parameters a and ρ corresponding to the 0.896Hz frequency point, x is the value of the parameter a found, y is the value of the parameter ρ found, and z is the value of the objective function; (b) is the amplitude-frequency curve obtained by substituting the optimized parameters a and ρ into the notch filter with feedback and without feedback structures. It can be seen that the addition of the feedback structure improves the performance of the notch filter; (c) and (d) correspond to the optimization process of 1.195Hz; (e) and (f) correspond to the optimization process of 1.494Hz. Feedback notch is used to remove the 3rd, 4th, and 5th respiratory harmonics in the radar heartbeat signal. The results before and after harmonic removal are shown as follows. Figure 12 As shown in Figure 1, (a) represents the time-domain radar heartbeat signal waveform before respiratory harmonic removal, (b) represents the frequency-domain radar heartbeat signal waveform before respiratory harmonic removal, and (c) represents the time-domain radar heartbeat signal waveform after respiratory harmonic removal. Subfigure (d) represents the frequency-domain radar heartbeat signal waveform after respiratory harmonic removal. The three frequency points corresponding to respiratory harmonics are marked with black dots. Comparing the radar heartbeat signal spectra before and after harmonic removal, it can be seen that the three frequency points of respiratory harmonics have been significantly suppressed. Comparing the radar heartbeat signal time-domain waveforms before and after harmonic removal, the radar heartbeat signal that was "squashed" by respiratory harmonics has been restored.

[0165] The present invention proposes a respiratory harmonic suppression system in a UWB impact bio-radar heartbeat signal, such as Figure 13 As shown, it includes a signal preprocessing module, a respiratory harmonic frequency acquisition module and a radar heartbeat signal acquisition module;

[0166] The signal preprocessing module is used to obtain data after high-frequency noise is removed based on the radar echo data after DC and baseline drift are removed, and to obtain the distance unit with the largest slow-time signal accumulation amount among all distance units based on the data after high-frequency noise is removed;

[0167] The respiratory harmonic frequency acquisition module is used to acquire the radar respiratory signal according to the distance unit with the largest slow time signal accumulation amount among all distance units, and acquire the respiratory harmonic signal mixed in the heartbeat according to the radar respiratory signal;

[0168] The radar heartbeat signal acquisition module is used to obtain signal correlation analysis results based on the respiratory harmonic signal and the multiple frequency signals of the respiratory fundamental frequency mixed in the heartbeat, and process the signal correlation analysis results to obtain the radar heartbeat signal after removing the respiratory harmonics, thereby realizing respiratory harmonic suppression.

[0169] An embodiment of the present invention provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned apparatus embodiments are implemented.

[0170] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0171] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0172] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0173] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0174] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0175] The present invention proposes a method for suppressing respiratory harmonics in UWB impact bioradar heartbeat signals. This method is based on a point-frequency respiratory harmonic removal method. Before each harmonic removal, an intelligent optimization algorithm is used to find the optimal structural parameters of the filter, ensuring maximum respiratory harmonic removal while minimizing loss to the heartbeat signal. Furthermore, this method removes respiratory harmonics based on the fundamental frequency of respiration, automatically locating and removing them. Furthermore, this method considers the processing of respiratory harmonics when they coincide with the main peak of the radar heartbeat signal.

[0176] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal, characterized in that: The steps include: Obtain data after high-frequency noise removal based on radar echo data after DC and baseline drift removal, and obtain a range unit with the largest slow-time signal accumulation amount among all range units based on the data after high-frequency noise removal; The radar respiration signal is obtained according to the distance unit with the largest slow time signal accumulation among all the distance units, and the respiratory harmonic signal mixed in the heartbeat is obtained according to the radar respiration signal; The signal correlation analysis results are obtained based on the respiratory harmonic signal mixed in the heartbeat and the multiple frequency signals of the respiratory fundamental frequency. The signal correlation analysis results are processed to obtain the radar heartbeat signal after removing the respiratory harmonics, thereby achieving respiratory harmonic suppression; The method for obtaining signal correlation analysis results is as follows: Where N is the length of the signal, R Hf(k) (n) is the respiratory harmonic signal mixed in the heartbeat, is the average value of the respiratory harmonic signal mixed in the heartbeat, R TPk (n) is the frequency multiplication signal obtained by interpolation of the respiratory fundamental frequency signal, and the obtained respiratory fundamental frequency signal R TP (n) Perform k-fold frequency interpolation to obtain the k-fold frequency time domain signal R of the radar breathing signal TPk (n), is the average value of the k-fold frequency time domain signal obtained by interpolating the respiratory fundamental frequency signal, p k is the obtained kth harmonic correlation coefficient.

2. The method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal according to claim 1, wherein: Get radar echo data R after removing DC and baseline drift DC The (m,n) method is as follows: Where Raw(m,n) is the unprocessed radar echo data, m and n represent the number of fast-time and slow-time samples, respectively. m represents the number of rows of radar echo data, and n represents the number of columns of radar echo data (1≤m≤M, 1≤n≤N). M represents the total number of fast-time samples, and N represents the total number of slow-time samples. The value of x ranges from 1 to (N-99).

3. The method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal according to claim 1, characterized in that: Get the data R after removing high-frequency noise LP The (m,n) method is as follows: R LP (m,n)=R DC (m,n)*H LP1 (t)(2) Among them, H LP1 (t) is the finite impulse response function of the low-pass filter, R DC (m,n) is the radar echo data after removing DC and baseline drift; The distance unit m1 with the largest slow-time signal accumulation among all distance units is obtained as follows: Where S(m) is the accumulated slow-time signal at the m-th distance unit.

4. The method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal according to claim 1, wherein: Get radar breathing signal R TP (n) as follows: R TP (n)=R LP (m1,n)*H LP2 (t)(4) Among them, H LP2 (t) is the finite impulse response function of the low-pass filter, R LP (m1,n) is the slow-time signal at the point where the accumulated amount of slow-time signal is the largest among all distance units; The method for obtaining the respiratory harmonic frequency f(k) is as follows: Among them, F TP (f) is the frequency domain respiratory signal after Fourier transform, F is the Fourier transform symbol, maxvalue is the maximum value solution function, f r is the fundamental frequency of breathing, F r is the amplitude corresponding to the fundamental frequency of breathing, f(k) is k times of the fundamental frequency of breathing, k is an integer, and the value is 3, 4, or 5.

5. The method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal according to claim 1, wherein: The method for obtaining the respiratory harmonic signal mixed in the heartbeat is as follows: R Hf(k) (n)=R HP (n)*H BPk (t) (6) Among them, H BPk (t) is the finite impulse response function of the bandpass filter for the kth harmonic, R HP (n) is the radar heartbeat signal containing respiratory harmonics; Get radar heartbeat signal R HP (n) as follows: R HP (n)=R LP (m1,n)*H BP (t) (7) Among them, H BP (t) is the finite impulse response function of the bandpass filter, R LP (m1,n) is the slow-time signal at the point where the accumulated amount of slow-time signal is the largest among all distance units.

6. The method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal according to claim 1, characterized in that: The method for obtaining the radar heartbeat signal after removing the respiratory harmonics is as follows: Signal correlation analysis results p k >K p When , the kth respiratory harmonic and the main peak of the heartbeat do not coincide; Signal correlation analysis results p k ≤K p When , the kth respiratory harmonic coincides with the main peak of the heartbeat; When the kth respiration harmonic coincides with the main peak of the heartbeat, the coincident harmonic frequency is retained as the radar heartbeat signal; When the kth respiratory harmonic does not coincide with the main peak of the heartbeat, the respiratory harmonic is removed to obtain the radar heartbeat signal after removing the respiratory harmonic interference; Among them, the threshold K is set p The settings are obtained by contact devices.

7. A system using the method for suppressing respiratory harmonics in a UWB impact bio-radar heartbeat signal according to any one of claims 1 to 6, characterized in that: include: A signal preprocessing module is used to obtain data after high-frequency noise is removed from the radar echo data after DC and baseline drift are removed, and to obtain a range unit with the largest slow-time signal accumulation amount among all range units based on the data after high-frequency noise is removed; a respiratory harmonic frequency acquisition module, wherein the respiratory harmonic frequency acquisition module is used to acquire a radar respiratory signal according to the distance unit with the largest slow time signal accumulation amount among all distance units, and to acquire a respiratory harmonic signal mixed in the heartbeat according to the radar respiratory signal; The radar heartbeat signal acquisition module is used to obtain signal correlation analysis results based on the respiratory harmonic signal and the multiple frequency signals of the respiratory fundamental frequency mixed in the heartbeat, process the signal correlation analysis results to obtain the radar heartbeat signal after removing the respiratory harmonics, and realize respiratory harmonic suppression.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for suppressing respiratory harmonics in a UWB impact bioradar heartbeat signal as claimed in any one of claims 1 to 6 are implemented.

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