A Human Respiration and Heartbeat Monitoring Method Based on Time-Domain Under-Sampling of Millimeter-Wave Radar

Abstract

The present invention discloses a human breathing and heartbeat monitoring method based on millimeter wave radar time domain undersampling, models the chest fluctuation caused by breathing and the chest micro-movement caused by heartbeat, and derives the mathematical analytical expression of the micro-Doppler signal corresponding to human breathing and heartbeat in combination with the millimeter wave radar signal system; on the basis of clarifying the radar echo modulation mechanism, a time domain undersampling method is proposed, and the radar modulation frequency is used as the sampling frequency, and the Doppler signal can be accurately extracted from the time domain difference frequency signal; according to the frequency bands of the breathing signal and the heartbeat signal, different filters are designed to separate the Doppler signal corresponding to breathing and the micro-Doppler signal corresponding to the heartbeat from the proposed signal; the breathing Doppler signal frequency and the heartbeat micro-Doppler signal frequency are calculated to obtain the human breathing frequency and the heartbeat frequency, so as to realize the human breathing and heartbeat monitoring. The present invention can accurately invert the frequency of human breathing and heartbeat in a non-contact situation.

Description

Technical Field

[0001] The present invention relates to millimeter-wave radar signal processing technology, and particularly to a method for monitoring human respiration and heartbeat based on time-domain undersampling of millimeter-wave radar. Background Art

[0002] Effective monitoring of human respiration and heartbeat is an important means to ensure health, which can effectively prevent various lung and heart diseases. However, traditional human respiration and heartbeat monitoring mainly rely on contact-type optoelectronic means such as electrocardiogram and phonocardiogram. The human body needs to be connected to the measurement device for a long time, which not only limits the application scenarios but also is costly. Existing non-contact measurement methods such as lasers and WIFI have problems such as low accuracy and high requirements for hardware devices.

[0003] As a detection component with ultra-high range resolution, millimeter-wave ultra-wideband radar can not only effectively measure the human respiration and heartbeat frequencies by accurately detecting the chest fluctuations but also has characteristics such as non-contact. Therefore, it is of great research significance to carry out research on non-contact human respiration and heartbeat monitoring methods based on millimeter-wave ultra-wideband radar. However, existing human respiration and heartbeat monitoring methods based on millimeter-wave ultra-wideband radar all rely on the phase change of the human chest echo signal. After obtaining the complete difference-frequency data, it is necessary to determine the target distance unit and perform multiple phase inversion calculations based on the fast Fourier transform, which is computationally complex and has large measurement errors. There is an urgent need for a respiration and heartbeat measurement method with small computational complexity and high accuracy. Summary of the Invention

[0004] Object of the Invention: The present invention provides a method for monitoring human respiration and heartbeat based on time-domain undersampling of millimeter-wave radar to overcome the problems of contact type, high cost, large computational amount, and limited accuracy existing in existing respiration and heartbeat monitoring means.

[0005] Technical Solution: A method for monitoring human respiration and heartbeat based on time-domain undersampling of millimeter-wave radar according to the present invention includes the following steps:

[0006] (1) Model the chest fluctuations caused by respiration and the chest micro-movements caused by heartbeat, and calculate the mathematical analytical expression of the chest displacement.

[0007] (2) Combine the millimeter-wave ultra-wideband radar signal system to deduce the mathematical analytical expression of the respiration and heartbeat micro-Doppler signal, and simulate the respiration and heartbeat micro-Doppler signal.

[0008] (3) Analyze the mathematical analytical relationship between intermediate-frequency signals in different modulation periods, clarify the working principle of time-domain undersampling, and use the time-domain undersampling method to extract the Doppler component in the intermediate-frequency signal to obtain the time-domain sampling signal of the Doppler signal modulated by micro-Doppler.

[0009] (4) Based on the different frequency band ranges of the Doppler frequency corresponding to the chest cavity undulation during breathing and the micro-Doppler frequency corresponding to the chest cavity micro-movement during heartbeat, design corresponding filters to separate the breathing Doppler signal and the heartbeat micro-Doppler signal;

[0010] (5) Use Fourier transform to calculate the spectra of the breathing Doppler signal and the heartbeat micro-Doppler signal, obtain the human breathing frequency and heartbeat frequency, and realize human breathing and heartbeat monitoring.

[0011] Further, the implementation process of modeling the chest cavity undulation caused by breathing in step (1) is as follows:

[0012] Model the human breathing signal as a simple sine signal, then the chest cavity undulation caused by the breathing signal is modeled as:

[0013] x r (t) = A r sin(2πf r t)

[0014] where A r and f r are respectively the amplitude and frequency of human breathing. Denote the initial radial distance from the radar to the human chest cavity as R0. When there is only breathing, the instantaneous radial distance between the human chest wall and the radar is:

[0015] x r (t) = R0 + A r sin(2πf r t).

[0016] Further, the implementation process of modeling the chest cavity micro-movement caused by heartbeat in step (1) is as follows:

[0017] The pulse signal generated by the ventricle is an exponential signal t0 is the pulse duration constant. Pass this signal through a second-order Butterworth filter, and set the cut-off frequency of the filter to f0. The filtered displacement is:

[0018]

[0019] where ω0 is the angular frequency corresponding to the filter cut-off frequency f0, A h is the human heartbeat amplitude, and the signal in x h (t) corresponds to one beat of the heartbeat. Then repeat this signal with a period of 1 / f h , f h is the human heartbeat frequency, and the model of the chest cavity micro-movement signal caused by continuous-time heartbeat is obtained.

[0020] Further, the implementation process of step (2) is as follows:

[0021] The displacement of the chest wall caused by the respiration and heartbeat signals is modeled as a superposition state, and the displacement of the chest wall caused by respiration and heartbeat is obtained as:

[0022]

[0023] The radar echo delay is expressed as:

[0024] τ = 2x r (t) / c

[0025] where c is the propagation speed of electromagnetic waves; τ changes with the chest displacement caused by respiration and heartbeat; the chest displacement caused by respiration will produce Doppler modulation, and the chest displacement caused by heartbeat will produce additional micro-Doppler modulation on the Doppler signal. Just extract the Doppler signal from the difference frequency signal of the radar to further solve the respiration and heartbeat frequencies.

[0026] Furthermore, the implementation process of step (2) is as follows:

[0027] The frequency of the radar transmitted signal is:

[0028]

[0029] where ΔF M is the modulation bandwidth, f m = 1 / T M is the modulation frequency, T M is the modulation period, and f0 is the carrier frequency;

[0030] The frequency of the received signal is:

[0031]

[0032] Using the relationship expression between frequency and phase φ = 2π∫fd t , the instantaneous phase difference between the transmitted signal and the received signal is obtained; within a single chirp period, the instantaneous phases of the transmitted signal and the received signal are respectively obtained.

[0033] When , the phase of the transmitted signal is:

[0034]

[0035] When , the phase expression of the transmitted signal is:

[0036]

[0037] where C is a constant;

[0038] From the continuity of the signal phase, it can be known that Then

[0039] When the phase of the received signal is:

[0040]

[0041] When i.e., the signal is in the regular region, the instantaneous phase difference between the transmitted signal and the received signal is:

[0042] Δφ1 = |φ R - φ T1 | = 2π(τΔF M f m t + f0τ)

[0043] The form of the difference frequency signal obtained by mixing down the transmitted signal and the echo signal is cos(Δφ n ), then the difference frequency signal in the regular region is:

[0044] cos(Δφ1) = cos2π(ΔF M f m τt + f0τ);

[0045] The difference frequency signal cos(Δφ1) in the regular region is written in the form of a Fourier trigonometric series:

[0046]

[0047] where

[0048]

[0049] Let:

[0050]

[0051] Then A n = a n cos(2πf0τ), B n = b n sin(2πf0τ), A0 = a0 cos(2πf0τ);

[0052] The Fourier trigonometric series decomposition of the regular region part of the difference frequency signal is:

[0053]

[0054] Taking samples of e(t) at the frequency f s = f m / p (p ∈ N * ), then:

[0055]

[0056] Among them, The frequency of is much smaller than the frequency of the Doppler signal cos(2πf0τ), and e(k) can be regarded as the Doppler signal modulated by

[0057] Ensure that f s = f m / p ≥ 2f d (p ∈ N * ), with f s = f m / p (p ∈ N * ) as the sampling rate, perform time-domain undersampling on the intermediate-frequency signal to extract the Doppler signal, and f d represents the Doppler frequency.

[0058] Furthermore, the implementation process of step (4) is as follows:

[0059] In order to eliminate the static interference and noise interference in the Doppler signal s d (t) modulated by the chest wall micro-motion caused by the heartbeat, first perform moving average denoising processing on s d (t) to obtain s dn (t); the Doppler frequency range generated by the chest wall undulation caused by breathing is 0.13 Hz to 0.4 Hz, while the micro-Doppler frequency range generated by the chest wall micro-motion caused by the heartbeat is 0.82 Hz to 3.3 Hz; two band-pass filters H r and H h with frequency ranges of 0.1 Hz to 0.5 Hz and 0.6 Hz to 4 Hz are respectively set; send s dn (t) into the designed filters H r and H h respectively. The output of filter H r is the Doppler signal s r (t) corresponding to the chest wall undulation during breathing, and the output of filter H r is the micro-Doppler signal s h (t) corresponding to the chest wall micro-motion during heartbeat.

[0060] Furthermore, the implementation process of step (4) is as follows:

[0061] Perform Fourier transforms on the separated Doppler signal s r (t) and micro-Doppler signal s h (t) respectively to obtain the spectra S r (f) and S h (f) of the two signals, and find the frequencies corresponding to the spectral peaks, which are respectively denoted as f r and f h, then the human respiratory rate RESP is 60·f r (times per minute), and the human heart rate HR is 60·f h , realizing the monitoring of human respiration and heart rate.

[0062] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention undersamples the time-domain echo at a very low sampling rate, and can directly extract accurate Doppler signals, and then separate the respiratory signal and the heart rate signal to realize the measurement of the respiratory rate and heart rate. It not only does not require obtaining complete difference-frequency data and multiple fast Fourier transforms, but also greatly reduces the hardware cost and computational complexity. Moreover, starting from the chest cavity fluctuations caused by respiration and heart rate, a micro-Doppler signal model is established, which can obtain higher accuracy while clarifying the mechanism; the present invention can inversely calculate the accurate frequencies of human respiration and heart rate by measuring the Doppler frequency and micro-Doppler frequency corresponding to the chest cavity fluctuations under non-contact conditions. Description of the Drawings

[0063] Figure 1 is the flowchart of the present invention;

[0064] Figure 2 is the time-domain waveform and spectrum of the chest cavity displacement caused by respiration;

[0065] Figure 3 is the time-domain waveform and spectrum of the chest cavity displacement caused by heart rate;

[0066] Figure 4 is the time-domain waveform and spectrum of the chest cavity displacement caused by respiration and heart rate;

[0067] Figure 5 is the time-domain waveform and spectrum of the intermediate-frequency signal of the 77GHz linear frequency-modulated radar;

[0068] Figure 6 is the time-domain waveform of the Doppler signal extracted by the time-domain undersampling method;

[0069] Figure 7 is the time-domain waveform of the Doppler signal after average denoising;

[0070] Figure 8 is the time-domain waveform and spectrum of the Doppler modulation signal caused by the chest cavity fluctuations during respiration;

[0071] Figure 9 is the time-domain waveform and spectrum of the micro-Doppler modulation signal caused by the chest cavity micro-motion during heart rate. Detailed Embodiment

[0072] The present invention will be further described in detail below with reference to the accompanying drawings.

[0073] As Figure 1As shown in the figure, the present invention proposes a non-contact human respiration and heartbeat monitoring method based on time-domain undersampling of millimeter-wave ultra-wideband radar, including the following steps:

[0074] Step 1, model the chest wall undulation caused by respiration and the chest wall micro-motion caused by heartbeat, calculate the mathematical analytical expression of the chest displacement, combine the millimeter-wave ultra-wideband radar signal system, deduce the mathematical analytical expression of the respiration and heartbeat micro-Doppler signal, and simulate the respiration and heartbeat micro-Doppler signal.

[0075] Millimeter-wave ultra-wideband radar has extremely high range resolution and can effectively detect the chest displacement caused by respiration and heartbeat. Human respiration includes inhalation and exhalation. During inhalation, the lungs expand, driving the chest wall to expand. During exhalation, the lungs contract, driving the chest wall to contract. The displacement of the chest wall caused by respiration is a simple periodic repetitive motion of expansion and contraction. The displacement amplitude of the chest wall caused by respiration is 4mm - 12mm, and the frequency is 0.13Hz - 0.4Hz. This displacement is relatively large compared to the wavelengths of 77GHz and 60GHz radars and has a relatively low frequency, being a low-speed target in the radar's perspective and is relatively easy to detect.

[0076] Model the human respiration signal as a simple sine signal, then the chest wall displacement caused by the respiration signal is modeled as:

[0077] x r (t) = A r sin(2πf r t)

[0078] where A r and f r are respectively the amplitude and frequency of human respiration. Denote the initial radial distance from the radar to the human chest as R0. When there is only respiration, the instantaneous radial distance between the human chest wall and the radar can be obtained as:

[0079] x r (t) = R0 + A r sin(2πf r t)

[0080] Human heart activity includes the contraction and relaxation of atrial muscle and ventricles. The frequency of the chest wall displacement caused by heartbeat is 0.82Hz - 3.3Hz, and the amplitude is less than 0.6mm, about one-tenth of that of respiration, being an extremely weak signal and is easily affected by respiration signals and other interferences.

[0081] Compared with the respiratory signal, the mechanism of the heartbeat is more complex, and the heartbeat signal model is also more complex. It can be modeled as a sine wave, a half-period sine pulse, a Gaussian pulse sequence, two adjacent pulses, etc. However, there are significant deviations between these signal models and the actual signals. The present invention proposes an improved heartbeat signal model, which can be briefly described as follows: during the heartbeat process, when the ventricle is in the systolic phase, it transmits a short pulse signal, which is filtered by the bones and body tissues. This process can be equivalent to filtering. The filtered pulse signal is transmitted to the chest wall, generating a perceptible chest wall displacement. Based on this process, the heartbeat signal is modeled. First, assume that the pulse signal generated by the ventricle is an exponential signal where t0 is the pulse duration constant. Pass this signal through a second-order Butterworth filter with the cut-off frequency of the filter set to f0, and the filtered displacement is obtained as follows:

[0082]

[0083] where ω0 is the angular frequency corresponding to the filter cut-off frequency f0, A h is the human heartbeat amplitude, and the signal in x h (t) corresponds to one beat of the heartbeat. Subsequently, repeat this signal with a period of 1 / f h , where f h is the human heartbeat frequency, and a continuous-time heartbeat signal model is obtained.

[0084] Model the chest wall displacement caused by the respiratory and heartbeat signals as a superposition state, and the chest wall displacement caused by respiration and heartbeat is obtained as follows:

[0085]

[0086] The radar echo delay can be expressed as:

[0087] τ = 2x r (t) / c

[0088] where c is the electromagnetic wave propagation speed, and τ changes with the chest displacement caused by respiration and heartbeat. Among them, the chest displacement caused by respiration will produce Doppler modulation, and the chest displacement caused by heartbeat will produce additional micro-Doppler modulation on the Doppler signal. Just extract the Doppler signal from the difference frequency signal of the chirp radar, and the respiration and heartbeat frequencies can be further solved.

[0089] Step 2: Analyze the mathematical analytical relationship between the intermediate frequency signals in different modulation periods, clarify the working principle of time-domain undersampling, and use the time-domain undersampling method to extract the Doppler component in the intermediate frequency signal to obtain the time-domain sampling signal of the Doppler signal with micro-Doppler modulation.

[0090] Taking the sawtooth frequency-modulated radar as an example, the frequency expression of the transmitted signal can be expressed as:

[0091]

[0092] where ΔF M is the modulation bandwidth, f m = 1 / T M is the modulation frequency, T M is the modulation period, and f0 is the carrier frequency.

[0093] The frequency expression of the received signal can be expressed as:

[0094]

[0095] Using the relationship expression between frequency and phase φ = 2π∫fd t , the instantaneous phase difference between the transmitted signal and the received signal can be obtained. Within a single linear frequency modulation period, the instantaneous phases of the transmitted signal and the received signal are respectively obtained.

[0096] When , the phase expression of the transmitted signal is:

[0097]

[0098] When , the phase expression of the transmitted signal is:

[0099]

[0100] where C is a constant.

[0101] From the continuity of the signal phase, it can be known that then

[0102] When , the phase expression of the received signal is:

[0103]

[0104] When , that is, when the signal is in the regular area, the instantaneous phase difference between the transmitted signal and the received signal is:

[0105] Δφ1 = |φ R - φ T1 | = 2π(τΔF M f m t + f0τ)

[0106] The form of the difference frequency signal obtained by mixing down the transmitted signal and the echo signal is cos(Δφ n),then the expression of the difference frequency signal in the regular region is:

[0107] cos(Δφ1) = cos2π(ΔF M f m τt + f0τ)

[0108] Although the difference frequency signal of a single period consists of a regular region and an irregular region, due to T M >> τ, the regular region part is mainly studied in the signal analysis process.

[0109] The difference frequency signal cos(Δφ1) in the regular region can be written in the form of a Fourier trigonometric series, and the expression is as follows:

[0110]

[0111] Where:

[0112]

[0113]

[0114] For the sake of concise writing, let:

[0115]

[0116] Then A n = a n cos(2πf0τ), B n = b n sin(2πf0τ), A0 = a0 cos(2πf0τ).

[0117] The Fourier trigonometric series decomposition expression of the regular region part of the difference frequency signal is:

[0118]

[0119] Taking f s = f m / p (p ∈ N * ) to sample e(t) at the frequency, then:

[0120]

[0121] Where, The frequency of is much smaller than the frequency of the Doppler signal cos(2πf0τ), and e(k) can be regarded as the modulated Doppler signal.

[0122] Therefore, taking f s = f m / p (p ∈ N *) is the sampling rate, and the Doppler signal can be extracted by undersampling the intermediate frequency signal in the time domain. However, it is still necessary to ensure that f s =f m / p≥2f d (p∈N * ), otherwise the extracted Doppler signal will be aliased. Here, f d Represents the Doppler frequency.

[0123] Step 3: Use the time domain undersampling method to extract the obtained s d (t) is the Doppler signal modulated by the chest micro-motion caused by the heartbeat. It is necessary to separate the Doppler signal caused by the chest ups and downs during breathing and the micro-Doppler signal caused by the chest micro-motion during heartbeat.

[0124] In order to eliminate d The static interference and noise interference in (t) are firstly d (t) Sliding average denoising process, get s dn (t), and then set two band-pass filters H with frequency ranges of 0.1Hz to 0.5Hz and 0.6Hz to 4Hz respectively. r and H h , the Doppler frequency range of the chest fluctuation caused by breathing is 0.13Hz~0.4Hz, and the micro-Doppler frequency range of the chest micro-movement caused by heartbeat is 0.82Hz~3.3Hz, so s dn (t) are respectively sent to the designed filter H r and H h , filter H r The output is the Doppler signal s corresponding to the chest rise and fall during breathing r (t), filter H r The output is the micro-Doppler signal s corresponding to the micro-movement of the chest during the heartbeat h (t).

[0125] Step 4: Separate the Doppler signal s r (t) and micro-Doppler signal s h (t), perform Fourier transform respectively to obtain the spectrum S of the two signals r (f) and S h (f), find the frequency corresponding to the spectrum peak, denoted as f r and f h , then the human respiratory rate RESP (Respiratory Rate) is 60·f r (beats / minute), the human heart rate HR (Heart Rate) is 60 f h (times / minute), thereby realizing human breathing and heartbeat monitoring.

[0126] To verify the effectiveness of the proposed solution of the present invention, the following simulation experiments were carried out. For the system parameters: the displacement amplitude of the chest wall caused by breathing is 6 mm, the frequency is 0.28 Hz, the displacement amplitude of the chest wall caused by the heartbeat is 0.6 mm, the frequency is 1.20 Hz, the carrier frequency of the chirp radar is 77 GHz, the modulation period is 25 ms, the frequency deviation is 300 MHz, and the time-domain undersampling frequency is the same as the modulation frequency, which is 40 Hz.

[0127] Figure 2 is the time-domain signal and spectrum of the chest displacement caused by breathing, Figure 3 is the time-domain signal and spectrum of the chest displacement caused by the heartbeat, Figure 4 is the time-domain signal and spectrum of the chest displacement caused by breathing and heartbeat; it can be found that the amplitude and frequency of the chest displacement signal are consistent with the preset values. Figure 5 is the time-domain waveform and spectrum of the intermediate-frequency signal of the 77-GHz chirp radar, Figure 6 is the time-domain waveform of the Doppler signal extracted based on the time-domain undersampling method, Figure 7 is for Figure 6 the result after averaging and denoising the waveform in Figure 8 is the time-domain waveform and spectrum of the breathing signal obtained after passing through a band-pass filter, Figure 9 is the time-domain waveform and spectrum of the heartbeat signal obtained after passing through a band-pass filter. It can be found that the frequency of the breathing signal is 0.2835 Hz, and the frequency of the heartbeat signal is 1.2024 Hz, which is basically consistent with the theoretical values. The corresponding breathing frequency is 17 times per minute, and the corresponding heartbeat frequency is 72 times per minute, which is within the normal frequency range of human breathing and heartbeat.

[0128] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. 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 belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for monitoring human respiration and heartbeat based on time-domain undersampling of millimeter-wave radar, characterized in that, Including the following steps: (1) Model the chest wall fluctuations caused by breathing and the chest wall micro-movements caused by heartbeat, and calculate the mathematical analytical expression of the chest displacement; (2) Combine the millimeter-wave ultra-wideband radar signal system, deduce the mathematical analytical expression of the breathing and heartbeat micro-Doppler signals, and simulate to obtain the breathing and heartbeat micro-Doppler signals; (3) Analyze the mathematical analytical relationship of the intermediate-frequency signals between different modulation periods, clarify the working principle of time-domain undersampling, use the time-domain undersampling method to extract the Doppler components in the intermediate-frequency signals, and obtain the time-domain sampling signals of the Doppler signals modulated by micro-Doppler; (4) Based on the fact that the Doppler frequency corresponding to the chest wall fluctuations during breathing and the frequency band ranges of the micro-Doppler frequencies corresponding to the chest wall micro-movements during heartbeat are different, design corresponding filters to separate the breathing Doppler signals and the heartbeat micro-Doppler signals; (5) Use Fourier transform to calculate the spectra of the breathing Doppler signals and the heartbeat micro-Doppler signals, obtain the human breathing frequency and heartbeat frequency, and realize the monitoring of human breathing and heartbeat.

2. The human respiration and heartbeat monitoring method based on millimeter-wave radar time-domain undersampling according to claim 1, wherein The implementation process of modeling the chest wall fluctuations caused by breathing in step (1) is as follows: Model the human breathing signal as a simple sine signal, then the chest wall fluctuations caused by the breathing signal are modeled as: x r x(t) = A r sin(2πft r t) Among them, A r and f r are the amplitude and frequency of human breathing respectively. Denote the initial radial distance from the radar to the human chest as R0. When there is only breathing, the instantaneous radial distance between the chest wall of the human body and the radar is obtained as follows: x r y(t) = R0 + A r sin(2πft r ) 3. A method for monitoring human respiration and heartbeat based on time-domain undersampling of millimeter-wave radar according to claim 1, characterized in that, The implementation process of modeling the chest wall micro-movements caused by heartbeat in step (1) is as follows: The pulse signal generated by the ventricle is an exponential signal t0 is the pulse duration constant. Pass this signal through a second-order Butterworth filter with the cut-off frequency of the filter set to f0, and the filtered displacement obtained is as follows: where ω0 is the angular frequency corresponding to the filter cut-off frequency f0, and A h is the human heartbeat amplitude, and the signal in x h (t) corresponds to one beat of the heartbeat. Subsequently, this signal is repeated with a period of 1 / f h , where f h is the human heartbeat frequency, and thus the model of the chest wall micro-motion signal caused by continuous-time heartbeat is obtained.

4. A human body respiration and heartbeat monitoring method based on millimeter-wave radar time-domain undersampling according to claim 1, characterized in that The implementation process of step (2) is as follows: Model the chest wall displacements caused by breathing and heartbeat signals as a superposition state, and obtain the chest wall displacements caused by breathing and heartbeat as: The radar echo delay is expressed as: τ = 2x r (t) / c where c is the electromagnetic wave propagation speed; τ varies with the chest displacements caused by breathing and heartbeat; the chest displacements caused by breathing will produce Doppler modulation, and the chest displacements caused by heartbeat will produce additional micro-Doppler modulation on the Doppler signals. Just extract the Doppler signals from the difference-frequency signals of the radar to further solve the breathing and heartbeat frequencies.

5. The human breathing and heartbeat monitoring method based on millimeter-wave radar time-domain undersampling according to claim 1, characterized in that The implementation process of step (2) is as follows: The frequency of the radar transmitted signal is: Among them, ΔF M is the modulation bandwidth, f m = 1 / T M is the modulation frequency, T M is the modulation period, and f0 is the carrier frequency; The frequency of the received signal is: Using the relationship expression between frequency and phase φ = 2π∫fd t , the instantaneous phase difference between the transmitted signal and the received signal is obtained; within a single chirp period, the instantaneous phases of the transmitted signal and the received signal are respectively obtained. When the phase of the transmitted signal is: When the phase expression of the transmitted signal is: where C is a constant; From the continuity of the signal phase, it can be known that then When the phase of the received signal is: When the instantaneous phase difference between the transmitted signal and the received signal is: Δφ1 = |φ R - φ T1 | = 2π(τΔF M f m t + f0τ) The form of the difference frequency signal obtained by down - mixing the transmitted signal and the echo signal is cos(Δφ n ), then the difference frequency signal in the regular area is: cos(Δφ1) = cos2π(ΔF M f m τt + f0τ); The difference-frequency signal cos(Δφ1) in the regular region is written in the form of a Fourier trigonometric series: where, Let: Then A n = a n cos(2πf0τ), B n = b n sin(2πf0τ), A0 = a0cos(2πf0τ); The Fourier trigonometric series decomposition of the regular region part of the difference-frequency signal is: With f s = f m / p (p ∈ N * ) sample the e(t) with the frequency, then: Among them, The frequency of is much smaller than the frequency of the Doppler signal cos(2πf0τ), and e(k) can be regarded as being modulated Doppler signal; Ensure f s = f m / p ≥ 2f d (p ∈ N * ), with f s = f m / p (p ∈ N * ) as the sampling rate, perform time-domain undersampling on the intermediate-frequency signal to extract the Doppler signal, where f d represents the Doppler frequency.

6. The human body respiration and heartbeat monitoring method based on millimeter-wave radar time-domain under-sampling according to claim 1, characterized in that The implementation process of step (4) is as follows: In order to eliminate the Doppler signal modulated by the chest micro-motion caused by the heartbeat d The static interference and noise interference in (t) are firstly d (t) Sliding average denoising process, get s dn (t); The Doppler frequency range of the chest fluctuation caused by breathing is 0.13Hz~0.4Hz, while the micro-Doppler frequency range of the chest micro-motion caused by heartbeat is 0.82Hz~3.3Hz; two bandpass filters H with frequency ranges of 0.1Hz~0.5Hz and 0.6Hz~4Hz are set respectively r and H h ; will s dn (t) are respectively sent to the designed filter H r and H h , filter H r The output is the Doppler signal s corresponding to the chest rise and fall during breathing r (t), filter H r The output is the micro-Doppler signal s corresponding to the micro-movement of the chest during the heartbeat h (t).

7. A human respiration and heartbeat monitoring method based on time-domain undersampling of millimeter-wave radar according to claim 1, characterized in that, The implementation process of step (4) is as follows: For the separated Doppler signal s r (t) and the micro-Doppler signal s h (t), perform Fourier transforms on them respectively to obtain the spectra S r (f) and S h (f). Find the frequencies corresponding to the spectral peaks, denoted as f r and f h respectively. Then the human breathing rate RESP is 60·f r (times / minute), and the human heart rate HR is 60·f h , realizing the monitoring of human breathing and heartbeats.

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