Millimeter wave radar adaptive transmitting power optimization method for vital sign monitoring

Abstract

The invention relates to a millimeter wave radar adaptive transmitting power optimization method for vital sign monitoring. The method comprises the following steps: adopting an FMCW-MIMO radar to carry out human body vital sign monitoring; processing the received echo signal reflected by each frame of human body corresponding to each frame of transmitting signal to obtain a discrete echo signal; preprocessing the discrete echo signal, and estimating a target distance, a speed and an angle of arrival by adopting CFAR (Constant False Alarm Rate) detection; according to the target arrival angle, receiving end beam forming is carried out on the received signal; phase extraction is carried out according to a range gate where the target distance is located, and after band-pass filtering is carried out on the extracted phase, vital sign signals including respiration and heart rate are extracted; and estimating signal power in an arrival angle region according to the arrival angle of the previous frame of data, and performing adaptive adjustment on the transmitting power of the next frame of radar, so that the power of the echo signal reflected by the human body corresponding to the frame meets the power requirement of CFAR detection. According to the invention, low-radiation and high-precision human body vital sign monitoring is realized.

Description

Technical Field

[0001] The present invention relates to the cross - field of MIMO radar technology and medical health technology, and relates to a method for optimizing the adaptive transmission power of a millimeter - wave radar for vital sign monitoring. Background Art

[0002] Array antenna technology has been widely used in fields such as radar, wireless communication, sonar, and navigation. According to the requirements of actual applications, antennas can have different arrangements, and the most basic can be divided into linear arrays and planar arrays. Compared with a single antenna, array antennas can achieve functions such as beam scanning, beamforming, and multi - beam. Domestic and foreign scientific researchers often conduct research on performance and applications according to the functional classification of array antennas, such as phased array (PA) antennas, frequency diverse array (FDA) antennas, adaptive antennas, and multiple - input multiple - output (MIMO) antennas, etc.

[0003] Millimeter - waves have been widely used in the fields of radar and communication. Millimeter - waves are electromagnetic waves with a frequency range between 30 GHz and 300 GHz. Their high frequency and short wavelength enable them to penetrate non - metallic materials such as clothing and skin, and have relatively small attenuation in these materials, enabling non - contact human body monitoring. When the human body moves and breathes, it will cause tiny displacements and changes. Millimeter - wave technology can detect these weak changes in motion and breathing patterns to achieve the perception of vital signs such as heart rate, respiratory rate, and motion state.

[0004] MIMO radar technology is very mature and is often used to achieve target detection, tracking, and imaging. Combining MIMO radar with millimeter - wave technology can achieve high - resolution, high - precision target detection, imaging, and positioning and other sensing technologies. The application of millimeter - wave - MIMO radar technology in the field of medical health is a popular and cutting - edge direction, but there are still challenges such as high - precision vital sign monitoring, moving target vital sign detection, and low - radiation detection. Summary of the Invention

[0005] In view of the above analysis, the present invention aims to disclose a method for optimizing the adaptive transmission power of a millimeter - wave radar for vital sign monitoring; to achieve low - radiation and high - precision human vital sign monitoring.

[0006] The present invention discloses a method for optimizing the adaptive transmission power of a millimeter - wave radar for vital sign monitoring, including:

[0007] Step S1: Use an FMCW - MIMO radar for human vital sign monitoring; process each frame of echo signal reflected by the human body corresponding to each frame of transmitted signal to obtain a discrete echo signal;

[0008] Step S2: Preprocess the discrete echo signal, and estimate the target distance, speed, and angle of arrival using CFAR detection;

[0009] Step S3: Perform receive beamforming on the received signal according to the target angle of arrival; extract the phase according to the range bin where the target distance is located, and after band-pass filtering the extracted phase, extract the vital sign signals including respiration and heart rate;

[0010] Step S4: Estimate the signal power within the angle of arrival region based on the angle of arrival of the previous frame of data, and perform adaptive adjustment of the radar transmit power of the next frame so that the echo signal power reflected by the human body corresponding to the next frame meets the power requirements of CFAR detection.

[0011] Further, after performing I fast-time samplings on the received echo signal reflected by the human body and slow-time coherent accumulation of K frames of data, a discrete echo signal is obtained.

[0012] Further, the step S2 includes:

[0013] Step S201: Perform multi-channel processing on the discrete echo signal, perform CFAR detection on each channel, and obtain the CFAR detection result; the CFAR detection result includes a CFAR detection matrix and the number of target detections;

[0014] Step S202: Perform target estimation on the CFAR detection result to obtain the estimated values of the target distance and speed;

[0015] Step S203: Use MUSIC to perform DOA estimation on multiple targets to obtain the target angle of arrival

[0016] Further, in the step S201, it includes:

[0017] 1) Perform two-dimensional range-Doppler FFT on the N*M channels of the discrete echo signal y (q) to obtain the range-Doppler spectrogram Y (q) [n], n = 1,..., M*N;

[0018] 2) Perform CFAR detection on the range-Doppler spectrogram Y (q) [n], n = 1,..., M*N, of each channel to obtain a CFAR mask matrix and a CFAR detection matrix;

[0019] 3) Calculate the number of non-zero elements of the CFAR mask matrix to obtain the number of target detections.

[0020] Further, in the step S202, for the range-Doppler spectrogram after CFAR detection, the range and velocity units with the maximum spectral energy are selected as the estimated range and velocity of the target.

[0021] Further, the step S3 includes:

[0022] Step S301: Perform receiving-end beamforming according to the estimated target arrival angle; perform spatial coherent accumulation on N*M channels to obtain the antenna weights and the accumulation result;

[0023] Step S302: Extract the target phase Ψ(k) from the accumulation result according to the range gate where the estimated target range is located;

[0024] Step S303: Perform spectral analysis using the filter bank method, and use two band-pass filters to filter the target phase Ψ(k) to obtain the respiration and heart rate signals respectively.

[0025] Further, the heart rate and respiration signals obtained by filtering;

[0026]

[0027] Among them, are the heart rate and respiration signals respectively; the function performs spectral peak search at 0.1 - 0.5 hz and 0.8 - 2.0 hz respectively.

[0028] Further, the step S4 includes:

[0029] Step S401: According to the target arrival angle estimated by MUSIC perform power estimation using the amplitude-phase estimation method to obtain the power of the estimated target arrival angle;

[0030] Step S402: Using the echo power of the farthest target that can be detected, calculate the estimated power factor according to the transmit power and the power of the estimated target arrival angle

[0031] Step S403: Determine the transmit power of the next frame using the power factor of the previous frame, the CFAR detection threshold, and the power upper limit.

[0032] Further, according to the target arrival angle the amplitude estimated using the amplitude-phase estimation method:

[0033]

[0034] Among them, is the virtual steering vector of the receiving array element at the angle and is the virtual steering vector of the transmitting array element at an angle ; s = [s0, s1, …, s M-1 ∈ R M×I is the transmitting signal vector of M transmitting array elements, and R ss is the autocorrelation matrix of the transmitting signal vector; the received sampling vector y ∈ R N×I , and R yy = yy H ; the superscript H is the vector transpose.

[0035] Furthermore, the transmitting power of the next frame is:

[0036]

[0037] where α (q) , η are the approximation factor and the amplitude factor respectively, N tn is the length of the training unit, p fa is the false alarm probability, Υ cfar is the CFAR detection threshold; P max is the maximum available transmitting power or the upper bound of the reasonable transmitting power that the human body can withstand; is the noise power estimate.

[0038] One of the beneficial effects that the present invention can achieve is as follows:

[0039] The millimeter-wave radar adaptive transmitting power optimization method for vital sign monitoring disclosed by the present invention realizes adaptive transmitting-end beamforming, continuously irradiates the target area, and enhances the signal-to-noise ratio of the received signal; it solves the problem that in the existing method, beamforming is only performed at the receiving end, and then multi-channel fusion is performed to achieve coherent accumulation, bringing gain in vital sign detection. However, when the main lobe of the transmitting beam is not aligned with the target, the signal-to-noise ratio and signal-to-interference ratio of the received signal will be too small;

[0040] An adaptive transmitting power control algorithm is proposed, which can effectively reduce the transmitting power of the radar and reduce electromagnetic radiation without reducing the monitoring accuracy; it reduces the harm of the electromagnetic radiation of the radar to the human body. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference signs denote the same components;

[0042] Figure 1 is the flow chart of the millimeter-wave radar adaptive transmitting power optimization method for vital sign monitoring in the embodiment of the present invention;

[0043] Figure 2 is the schematic diagram of a uniform linear array with M transmitters and N receivers in the embodiment of the present invention;

[0044] Figure 3 Schematic diagram of implementing an orthogonal signal set using TD-LFCW signals in an embodiment of the present invention. Specific implementation manners

[0045] The following specifically describes the preferred embodiments of the present invention with reference to the accompanying drawings, where the accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention.

[0046] An embodiment of the present invention discloses a method for optimizing the adaptive transmission power of a millimeter-wave radar for vital sign monitoring, as Figure 1 shown, including:

[0047] Step S1: Use an FMCW-MIMO radar to monitor human vital signs; process each frame of echo signal reflected by the human body corresponding to each frame of transmitted signal to obtain a discrete echo signal;

[0048] Step S2: Preprocess the discrete echo signal, and use CFAR detection to estimate the target distance, speed, and angle of arrival;

[0049] Step S3: Perform receiving-end beamforming on the received signal according to the target angle of arrival; extract the phase according to the range gate where the target distance is located, and after band-pass filtering the extracted phase, extract vital sign signals including respiration and heart rate;

[0050] Step S4: Estimate the signal power within the angle-of-arrival region based on the angle of arrival of the previous frame of data, and adaptively adjust the transmission power of the radar for the next frame so that the echo signal power reflected by the human body corresponding to the next frame meets the power requirements of CFAR detection.

[0051] Specifically, in the FMCW (Frequency Modulated Continuous Wave)-MIMO (Multi Input Multi Output) radar adopted in this embodiment, the FMCW signal is a signal whose frequency increases linearly with time. Using FMCW with a carrier frequency in the millimeter-wave band, it has the characteristic of a large "time-band product" and thus has high range resolution. As Figure 2 shown, assuming a centralized M-transmit N-receive uniform linear array, the time-domain expression of the continuous frequency-modulated signal transmitted by the m-th transmit array element is:

[0052]

[0053] where f0 is the carrier frequency, φ m (t) is the baseband waveform. Assuming that the transmit resistance has been normalized, P T (T) is the transmission power.

[0054] Let the set of transmitted signals be an orthogonal signal set. There is

[0055]

[0056] As Figure 3 shown, if the TD-LFCW (Time Division - Linear Frequency Modulated Continuous Wave) signal is adopted to implement the orthogonal signal set, then there is

[0057]

[0058] where the frequency modulation period is T c , the frequency modulation bandwidth is B, and the frequency modulation slope is γ = B / T c , and the frame period is T s = MT c .

[0059] There is a point target located at the far field (r, θ) with a radial velocity of v. The time delay from the transmitting element to the receiving element is:

[0060]

[0061] Assume that in the k-th frame, that is, when t ∈ (kT s , (k + 1)T s ), the transmitting power of the transmitting element is fixed, that is, P T (T) = P(k), then the received signal of the n-th element is:

[0062]

[0063] where the received signal power is:

[0064]

[0065] ω D (t; τ(t)) is the Doppler phase, and here there is:

[0066]

[0067] a n，m (θ) is the steering vector term between element n and element m, and here there is:

[0068]

[0069] Assume that there is Gaussian white noise in the receiver. The received data in the k-th frame is represented as multi-channel data:

[0070]

[0071] where, is a Gaussian random process, and the transmitting steering vector, receiving steering vector, and transmitted signal vector are as follows:

[0072]

[0073]

[0074]

[0075] Equivalent N receiving array elements to a virtual array of N*M array elements, and the virtual steering vector can be expressed as:

[0076]

[0077] Then the received signal is re-expressed as:

[0078]

[0079] At this time, the received data of the k-th frame

[0080] Assume that there are J targets located at different distances, different angles, and different scattering coefficients, and the received signal is rewritten as:

[0081]

[0082] It can be seen that in the received signal, the received power is related to the transmitted power, the scattering coefficient, the target distance, and the number of targets.

[0083] In a specific scheme of this embodiment, the signal source is a linear continuous frequency modulation wave signal, and the time-domain expression of the signal is:

[0084]

[0085] where f0 = 77 GHz is the carrier frequency, and γ = B / T c is the frequency modulation rate. Let the pulse duration T c be 10 ms, the frequency modulation bandwidth is B = 4 GHz, and the number of coherent integration frames is K = 8;

[0086] The number of array elements of the phased array structure adopted is M = 8, and the array element spacing d Tx is half the wavelength λ / 2, and the delay time between array elements is T c . The number of array elements of the receiving array is N = 8, and the array element spacing is half the wavelength.

[0087] Specifically, in step S1, after fast-time sampling and slow-time coherent integration of the received echo signal reflected by the human body, a discrete echo signal is obtained;

[0088] For a single target in space, I discrete-time signals [y (k) (0), y (k) (1), …, y (k) (I - 1)] ∈ R NM×I are obtained by performing I fast-time samplings on the k-th frame echo; the frame data expression of the k-th frame echo is:

[0089]

[0090] To improve the Doppler resolution, the frame data y (k) is coherently integrated over K frames in the slow-time domain to obtain the accumulated discrete echo signal;

[0091] The data obtained after coherently integrating K frames is represented as the q-th frame:

[0092]

[0093] where y (q) ∈ R NM×I×K , t ∈ [q * KT s , (q + 1) * KT s .

[0094] Specifically, step S2 includes:

[0095] Step S201: Perform multi-channel processing on the discrete echo signal, perform CFAR detection on each channel, and obtain the CFAR detection result; the CFAR detection result includes the CFAR detection matrix and the number of target detections;

[0096] Specifically, it includes:

[0097] 1) Perform range-Doppler 2D FFT on the N * M channels of the discrete echo signal y (q) respectively to obtain the range-Doppler spectrogram;

[0098]

[0099]

[0100] Y (q) represents the matrix composed of fast time - slow time to perform 2D-FFT transformation to obtain the range-Doppler spectrogram; u is the range, and v is the velocity.

[0101] 2) Perform CFAR detection on the range-Doppler spectrogram Y (q) [n], n = 1, …, M * N, of each channel to obtain the CFAR mask matrix and the CFAR detection matrix;

[0102] CFAR detection threshold where, Ntn is the training unit length, p fa is the false alarm probability;

[0103] According to the detection threshold Υ cfar [n] to detect Y (q) [n] to obtain the CFAR mask matrix Θ cfar :

[0104] Θ cfar [n] = Y (q) [n] > Υ cfar [n]? 1:0;

[0105] The elements of the CFAR mask matrix Θ cfar are 0 or 1;

[0106] Multiply the CFAR mask matrix with Y (q) [n] to obtain the final CFAR detection matrix;

[0107] Y cfar [n] = Θ cfar [n] ⊙ Y (q) [n];

[0108] where "⊙" represents the Hadamard product.

[0109] 3) Count the number of non-zero elements in the CFAR mask matrix to obtain the target detection number;

[0110] Target detection number

[0111] Step S202: Perform target estimation on the CFAR detection result to obtain the distance and speed estimation values of the target;

[0112] For the range-Doppler spectrogram after CFAR detection, select the range and speed units with the maximum spectral energy as the estimation of the target. Assume that the resolutions of range and speed are R res , V res , respectively. The range and speed estimation values have the following formula:

[0113]

[0114] Step S203: Perform DOA estimation on multiple targets using MUSIC to obtain the target arrival angle

[0115] Calculate the sampling covariance matrix of the discrete sampled echo signal

[0116] R yy = yy H ;

[0117] Perform singular value decomposition on the sampled covariance matrix R yy and arrange the singular values in descending order. Select the first N tar singular values that are the same as the number of target detections N tar . There are

[0118]

[0119] where the matrix Q s , Q n are respectively called the signal subspace matrix and the noise subspace matrix;

[0120] The result of DOA estimation for multiple targets using MUSIC is the target arrival angle

[0121]

[0122]

[0123] where the range of θ is -π / 2 to π / 2.

[0124] Specifically, step S3 includes:

[0125] Step S301, perform receiver beamforming according to the estimated target arrival angle; perform spatial coherence accumulation on N*M channels to obtain the antenna weights and the accumulation result;

[0126] The antenna weights for N*M channels are:

[0127]

[0128] The accumulation result is:

[0129]

[0130] Step S302, according to the range gate where the estimated target distance is located, extract the phase of the accumulation result to obtain the target phase Ψ(k);

[0131] From the data preprocessing, it can be known that the range gate where the target is located is So the target phase Ψ(k) obtained by extracting the phase of is:

[0132]

[0133] where the phase extraction function x is the real part of the complex number, and y is the imaginary part of the imaginary number.

[0134] Step S303: Perform spectral analysis using the filter bank method. Filter the target phase Ψ(k) with two band-pass filters to obtain the respiration and heart rate signals respectively.

[0135] The heart rate and respiration signals obtained by filtering;

[0136]

[0137] Among them, are the heart rate and respiration signals respectively; the function Performs spectral peak search at 0.1 - 0.5 hz and 0.8 - 2.0 hz respectively.

[0138] Specifically, in step S4, it includes:

[0139] Step S401: According to the target arrival angle obtained by MUSIC estimation Using the amplitude and phase estimation (APES) approach, Estimate the power of the angle;

[0140] According to the target arrival angle The amplitude estimated using the amplitude and phase estimation method:

[0141]

[0142] Among them, Is the virtual steering vector of the receiving array element at the angle , Is the virtual steering vector of the transmitting array element at the angle ; The transmission signal vector s of M transmitting array elements = [s0, s1,..., s M-1 ∈ R M×I ; R ss Is the autocorrelation matrix of the transmission signal vector; The received sampling vector y ∈ R N×I , R yy = yy H ; The superscript H is the vector transpose.

[0143] The estimated amplitude The method of is to estimate the power.

[0144] Step S402: Use the echo power of the farthest detectable target, and calculate the estimated power factor according to the transmission power and the power estimated by the target arrival angle

[0145] According to the radar equation, the relationship between the transmission power and the received power can be known, and the power factor Is defined as:

[0146]

[0147] It can be seen that the power factor is related to the target distance, the transmission frequency, and the transmitting and receiving antenna gains.

[0148] Assume that there are K targets, and the farthest detected target is the Jth one, and the echo power is According to the transmission power and the estimated power Obtain the estimation of the power factor As follows:

[0149]

[0150] Step S403: Determine the transmission power of the next frame by using the power factor of the previous frame, the CFAR detection threshold, and the power upper limit.

[0151] Assume that the power factor remains unchanged between adjacent frames, and make a restriction on the transmission power P T (q + 1):

[0152] a) The transmission power is less than the maximum available transmission power or the reasonable upper bound of the transmission power that the human body can withstand, P max ;

[0153] b) The transmission power enables the farthest target to be detected by CFAR.

[0154] That is, it satisfies the following formula:

[0155]

[0156] From the asymptotic stability, according to the above inequality, the transmission power P T (q + 1) of one frame is:

[0157]

[0158] Among them, α (q) , η are the approximation factor and the amplitude factor respectively, N tn is the length of the training unit, p fa is the false alarm probability, Υ cfar is the CFAR detection threshold; P max is the maximum available transmission power or the reasonable upper bound of the transmission power that the human body can withstand; is the noise power estimation.

[0159] More specifically, the approximation factor or

[0160] The amplitude factor is related to the detection threshold. Usually, η = 1.5, which means that the transmit power should be such that the echo power in the target area of interest is 1.5 times higher than the CFAR threshold.

[0161] In summary, the millimeter-wave radar adaptive transmit power optimization method for vital sign monitoring disclosed in the embodiments of the present invention realizes adaptive transmit-end beamforming, continuously irradiates the target area, and enhances the signal-to-noise ratio of the received signal; it solves the problem that in the existing method, beamforming is only performed at the receiving end, and then multi-channel fusion is carried out to achieve coherent accumulation, bringing gain in vital sign detection. However, when the main lobe of the transmit beam is not aligned with the target, the signal-to-noise ratio and signal-to-interference ratio of the received signal will be too small.

[0162] An adaptive transmit power control algorithm is proposed. Without reducing the monitoring accuracy, it effectively reduces the transmit power of the radar and reduces electromagnetic radiation; it reduces the harm of the electromagnetic radiation of the radar to the human body.

[0163] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. An adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring, characterized in that, Including: Step S1: Use an FMCW-MIMO radar for human vital sign monitoring; Process each frame of echo signal reflected by the human body corresponding to each frame of transmitted signal to obtain a discrete echo signal; Step S2: Preprocess the discrete echo signal, and use CFAR detection to estimate the target distance, speed, and angle of arrival; Step S3: Perform receive beamforming on the received signal according to the target angle of arrival; Extract the phase according to the range gate where the target distance is located, and after band-pass filtering the extracted phase, extract vital sign signals including respiration and heart rate; Step S4: Estimate the signal power within the angle-of-arrival region based on the angle of arrival of the previous frame of data, and adaptively adjust the radar transmit power of the next frame so that the echo signal power reflected by the human body corresponding to the next frame meets the power requirements of CFAR detection.

2. The adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring according to claim 1, characterized in that, After performing I fast-time samplings on the received echo signal reflected by the human body and slow-time coherent accumulation of K frames of data, a discrete echo signal is obtained.

3. The adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring according to claim 2, characterized in that, The said step S2 includes: Step S201: Perform multi-channel processing on the discrete echo signal, perform CFAR detection on each channel, and obtain the CFAR detection result; The CFAR detection result includes a CFAR detection matrix and the number of target detections; Step S202: Perform target estimation on the CFAR detection result to obtain the estimated values of the target distance and speed; Step S203: Perform DOA estimation on multiple targets using MUSIC to obtain the target arrival angles 4. The adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring according to claim 3, characterized in that, In the said step S201, it includes: 1) Perform two-dimensional range-Doppler FFT on the N*M channels of the discrete echo signal y (q) to obtain the range-Doppler spectrogram Y (q) [n], where n = 1, …, M*N; 2) For the range-Doppler spectrogram Y (q) [n] of each channel, where n = 1, …, M*N, perform CFAR detection to obtain the CFAR mask matrix and the CFAR detection matrix; 3) Calculate the number of non-zero elements of the CFAR mask matrix to obtain the number of target detections.

5. The adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring according to claim 3, characterized in that, In the said step S202, for the range-Doppler spectrogram after CFAR detection, select the range and speed units with the maximum spectral energy as the estimated distance and speed of the target.

6. The adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring according to claim 3, characterized in that, The said step S3 includes: Step S301: Perform receive beamforming according to the estimated target angle of arrival; Perform spatial coherent accumulation on N*M channels to obtain the antenna weights and the accumulation result; Step S302: Extract the target phase Ψ(k) from the accumulation result according to the range gate where the estimated target distance is located; Step S303: Use the filter bank method for spectral analysis, and use two band-pass filters to filter the target phase Ψ(k) to obtain the respiration and heart rate signals respectively.

7. The adaptive transmission power optimization method for millimeter-wave radar used in vital sign monitoring according to claim 6, characterized in that, The heart rate and respiration signals obtained by filtering; Among them, are the heart rate and respiratory signal respectively; the function performs spectral peak searches at 0.1 - 0.5 hz and 0.8 - 2.0 hz respectively.

8. The method for optimizing the adaptive transmission power of a millimeter-wave radar for vital sign monitoring according to claim 6, wherein The said step S4 includes: Step S401, the target arrival angle estimated according to MUSIC Use the amplitude-phase estimation method to perform power estimation to obtain the power of the target arrival angle estimation; Step S402: Calculate the estimated power factor based on the echo power of the farthest detectable target, the transmitted power, and the power estimated by the target arrival angle Step S403: Determine the transmit power of the next frame by using the power factor, CFAR detection threshold, and power upper limit of the previous frame.

9. The method for optimizing the adaptive transmission power of a millimeter-wave radar for vital sign monitoring according to claim 8, wherein According to the angle of arrival of the target The amplitude estimated by the amplitude-phase estimation method: Among them, is the virtual steering vector of the receiving array element at angle ; is the virtual steering vector of the transmitting array element at angle ; s = [s0, s1,..., s M-1 ∈ R M×I is the transmission signal vector of M transmitting array elements, and R ss is the autocorrelation matrix of the transmission signal vector; the received sampling vector y ∈ R N×I , and R yy = yy H ; the superscript H represents the vector transpose.

10. The method for optimizing the adaptive transmission power of a millimeter-wave radar for vital sign monitoring according to claim 9, wherein The transmit power of the next frame is: Among them, α (q) and η are the approximation factor and the amplitude factor respectively, N tn is the length of the training unit, p fa is the false alarm probability, Υ cfar is the CFAR detection threshold; P max is the maximum available transmit power or the upper bound of the reasonable transmit power that the human body can withstand; is the noise power estimate.

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