Radar-based human respiratory information extraction method, device, equipment and medium

Abstract

The application discloses a human body breathing information extraction method, device and equipment based on radar and a medium. The method comprises the following steps: mixing and filtering echo signals and transmission signals to obtain original data, wherein the transmission signals are radio frequency signals transmitted by an X-band radar, and the echo signals are signals received by an antenna; processing the original data, and determining whether there is a stationary human body according to the processed data; performing phase correction on the original data with the stationary human body, and extracting phase signals; performing phase unwrapping on the extracted phase signals; extracting a chest wall displacement-time signal from the unwrapped phase signals, and obtaining a breathing frequency of a human body target. Compared with the prior art, the application has a better anti-interference effect in extracting breathing information from echo signals of an X-band radar.

Description

Technical Field

[0001] This invention relates to a radar-based method, apparatus, equipment, and medium for extracting human respiratory information, belonging to the field of human respiratory frequency detection technology. Background Technology

[0002] A key aspect of detecting human respiratory rate is ensuring the presence of a stationary human body and ruling out other human presence to guarantee measurement accuracy. The X-band includes the ISM band, which holds significant research potential. X-band radar has a longer wavelength than millimeter-wave radar, giving it an advantage in penetrating media such as rain, snow, and fog. This advantage in obstacle penetration further enhances the practicality of X-band radar.

[0003] Unlike FMCW radar, unmodulated continuous wave radar can locate and determine the position of a person and the presence of multiple people. One key aspect is excluding multiple people. This paper proposes using a neural network approach, training the network to recognize a single, stationary person.

[0004] Currently, most non-contact respiratory information extraction methods utilize millimeter-wave radar, obtaining phase information from human echo signals through unwinding. However, X-band radar has lower resolution than millimeter-wave radar, resulting in poorer recognition of chest wall displacement, and noise significantly impacts phase information. The commonly used unwinding method is the extended-DACM algorithm. Extended-DACM uses two approximation processes: first, forward differencing to replace differentiation; second, rectangular summation to approximate integration. These two approximations limit DAM's approximation to high sampling rates. At lower sampling rates, significant errors occur. Furthermore, while forward differencing is used for phase extraction via cumulative summation, this process accumulates noise, significantly affecting demodulation performance for signals with low signal-to-noise ratios.

[0005] Therefore, existing technologies are suitable for high-frequency, high-sampling-rate radars, but their effectiveness in extracting breathing information is poor for radars with low operating frequencies and low sampling rates. Summary of the Invention

[0006] In view of this, the present invention provides a method, device, computer equipment and storage medium for extracting human respiratory information based on radar, which has a better anti-interference effect when extracting respiratory information from the echo signal of X-band radar compared with the prior art.

[0007] The first objective of this invention is to provide a radar-based method for extracting human respiratory information.

[0008] The second objective of this invention is to provide a radar-based device for extracting human respiratory information.

[0009] A third objective of this invention is to provide a computer device.

[0010] A fourth objective of this invention is to provide a storage medium.

[0011] The first objective of this invention can be achieved by adopting the following technical solution:

[0012] A radar-based method for extracting human respiratory information, the method comprising:

[0013] The echo signal and the transmitted signal are mixed and filtered to obtain the original data. The transmitted signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna.

[0014] The raw data is processed, and the presence of a stationary human body is determined based on the processed data.

[0015] Phase correction is performed on the raw data containing stationary human bodies to extract the phase signal;

[0016] Phase unwrapping is performed on the extracted phase signal;

[0017] The chest wall displacement-time signal is extracted from the unwound phase signal to obtain the respiratory rate of the human target.

[0018] Furthermore, the mixing and filtering of the echo signal and the transmitted signal to obtain the original data includes:

[0019] The echo signal and the transmitted signal are mixed to obtain the intermediate frequency signal, as shown in the following formula:

[0020]

[0021] Where τ is the time difference between the transmitted signal and the echo signal. It is phase noise of a continuous wave;

[0022] The intermediate frequency signal is converted from analog to digital to obtain the original data.

[0023] Furthermore, the processing of the original data, and the determination of whether a stationary human body exists based on the processed data, includes:

[0024] Perform a short-time Fourier transform on the original data to obtain the spectrum;

[0025] The spectrum is input into the trained human body recognition network to determine whether a live body exists. If a live body exists, it is determined whether the live body is in a static state. If it is in a static state, it is determined that a static human body exists.

[0026] The human body recognition network is a multi-layer convolutional neural network. After training, the human body recognition network can detect living bodies and identify their living states, which include both moving and stationary states.

[0027] Furthermore, the phase correction of the original data containing stationary human bodies is implemented using a least-squares ellipse fitting algorithm, including:

[0028] The echo signal and the transmitted signal are split into I / Q paths. The echo signal is mixed from RF to baseband signal, and the output baseband signal is as follows:

[0029]

[0030]

[0031] Among them, the I / Q signals are represented as ellipses in the plane; DC I DC Q It is the two centers of the DC bias ellipse, A I A Q These are the radii of the major and minor axes of the ellipse. θ0 is the angle of relative rotation of the ellipse due to I / Q phase imbalance, x(t) is the signal to be wound, and θ0 is the initial phase.

[0032] The required I is estimated using the least squares elliptic fitting algorithm. c / Q c Signal,I c / Q c The signal is represented by the following formula:

[0033]

[0034] Furthermore, the phase unwinding of the extracted phase signal is implemented using a phase unwinding algorithm based on adaptive extended Kalman filtering;

[0035] In the adaptive extended Kalman filter, each update process also updates the observation noise variance matrix and the process noise variance matrix. This updating of the observation noise variance matrix and the process noise variance matrix includes:

[0036] Based on residual adaptive estimation of the observation noise variance matrix, including:

[0037] Define residual ε k+1 The difference between the actual measured value and the estimated value in step k+1 is expressed by the following formula:

[0038]

[0039] Where R is the observation noise variance matrix, H is the state observation matrix, and P - E[·] represents the covariance between the true and predicted values, and E[·] represents the statistical average.

[0040] calculate By statistical averaging over time and introducing a forgetting factor of 0 < α ≤ 1, the observation noise variance matrix is ​​updated using the following formula:

[0041]

[0042] The noise variance matrix of the adaptive estimation process based on the updated difference includes:

[0043] Define the update difference r as follows:

[0044]

[0045] The process noise variance matrix is ​​updated using the following formula:

[0046] Q k+1 =αQ k+1 +(1-α)(K(k+1)r k+1 r k+1 T K(k+1) T )

[0047] Where Q is the process noise variance matrix.

[0048] Furthermore, the Kalman filter model includes state equations and observation equations;

[0049] Based on the motion characteristics of the chest wall displacement, the state equation is obtained as follows:

[0050]

[0051] Where, x p (k), x v (k) and x a (k) corresponds to the phase, velocity and acceleration of the phase transition of x(k), respectively; dt is the sampling interval; u(k) is the estimated value of the true phase gradient; and w(k) is the estimation error of the phase gradient.

[0052] Using the real and imaginary parts of the normalized complex signal as two observations of the phase, the observation equation is obtained as follows:

[0053]

[0054] Where v(k) is the observation error of the real and imaginary parts of the complex observation.

[0055] Furthermore, the step of extracting the chest wall displacement-time signal from the unwound phase signal to obtain the respiratory rate of the human target includes:

[0056] The spectrum is obtained by performing a fast Fourier transform on the unwound phase signal;

[0057] Based on the respiratory rate being between 0.15 and 0.5 Hz, the spectrum is filtered to obtain the signal within the filtered frequency band; the maximum value of the spectrum within the filtered frequency band is found, and the number of breaths per minute is calculated as follows:

[0058] HB=60×f b

[0059] Where HB is the number of breaths per minute, f b This represents the maximum value of the spectrum within the filtered frequency band.

[0060] The second objective of this invention can be achieved by adopting the following technical solution:

[0061] A radar-based human respiratory information extraction device, the device comprising:

[0062] The acquisition module is used to mix and filter the echo signal and the transmitted signal to obtain the raw data. The transmitted signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna.

[0063] The judgment module is used to process the raw data and determine whether there is a stationary human body based on the processed data.

[0064] The phase correction module is used to perform phase correction on raw data containing stationary human bodies and extract phase signals;

[0065] The phase unwinding module is used to unwind the extracted phase signal.

[0066] The extraction module extracts the chest wall displacement-time signal from the unwound phase signal to obtain the respiratory rate of the human target.

[0067] The third objective of this invention can be achieved by adopting the following technical solution:

[0068] A computer device includes a processor and a memory for storing processor-executable programs, characterized in that when the processor executes the program stored in the memory, it implements the above-described method for extracting human respiratory information.

[0069] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0070] A storage medium storing a program, which, when executed by a processor, implements the above-described method for extracting human respiratory information.

[0071] The present invention has the following advantages over the prior art:

[0072] 1. This invention transmits and receives electromagnetic waves using an X-band radar. It mixes and filters the echo signal and the transmitted signal to obtain raw data. The raw data is then processed to determine the state of the human body within the radar's range, identify stationary human bodies, perform phase correction on the raw data of stationary human bodies, and then use a phase unwinding algorithm to obtain unambiguous phase information to extract the chest wall displacement-time signal, thereby obtaining the respiratory frequency of the human target. This invention has a better anti-interference effect.

[0073] 2. The phase unwinding algorithm of this invention uses an adaptive extended Kalman filter phase unwinding algorithm, which has a better denoising effect than the extended-DACM algorithm. The adaptive algorithm can solve the dependence of Kalman filter on the setting of observation noise and process noise values, making the algorithm more portable and obtaining a more accurate result. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0075] Figure 1 This is a flowchart of the radar-based human respiratory information extraction method according to Embodiment 1 of the present invention.

[0076] Figure 2 This is a structural diagram of the human body recognition network in Embodiment 1 of the present invention.

[0077] Figure 3 The figure shows the simulation results of the adaptive extended Kalman filter algorithm of Embodiment 1 of the present invention compared with the existing DAM algorithm, EKF algorithm and the true value.

[0078] Figure 4 This is a structural block diagram of the radar-based human respiratory information extraction device according to Embodiment 2 of the present invention.

[0079] Figure 5 This is a structural block diagram of the computer device according to Embodiment 3 of the present invention. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] Example 1:

[0082] like Figure 1 As shown in the figure, this embodiment provides a radar-based method for extracting human respiratory information, which includes the following steps:

[0083] S101. Mix and filter the echo signal and the transmitted signal to obtain the original data.

[0084] In this embodiment, a continuous wave radar system operating at 5.8 GHz is built using a USRP B210. This system is a single-transmitter, single-receiver radar system. The antenna is a narrowband antenna operating near 5.8 GHz. When collecting data on stationary human bodies, the human body is 0.6 m away from the antenna, with the chest facing the antenna. When collecting data on various human activity states, the human body moves within a 2-meter range of the radar.

[0085] This embodiment uses X-band radar to transmit radio frequency signals, specifically transmitting unmodulated continuous wave signals, which are used as the transmitted signal, as shown in the following formula:

[0086]

[0087] The echo signal is received using an antenna, and the echo signal is as follows:

[0088]

[0089] Where t is time, and τ is the time difference between the transmitted and received signals. It is the phase noise of a continuous wave.

[0090] In this embodiment, the echo signal and the transmitted signal are mixed and filtered to obtain the original data, including:

[0091] S1011. Mix the echo signal and the transmitted signal to obtain the intermediate frequency signal, as shown in the following formula:

[0092]

[0093] S1012. Perform analog-to-digital conversion on the intermediate frequency signal to obtain the original data.

[0094] S102. Process the raw data and determine whether there is a stationary human body based on the processed data.

[0095] Further, step S102 includes:

[0096] S1021. Perform a short-time Fourier transform on the original data to obtain the time-Doppler spectrum, which is used as the spectrum.

[0097] S1022. Input the spectrum into the trained human body recognition network to determine whether there is a living body. If there is a living body, determine whether the living body is in a static state. If it is in a static state, determine that there is a static human body.

[0098] The human body recognition network in this embodiment is a three-layer convolutional neural network, such as... Figure 2 As shown, after training, the human body recognition network can detect liveness and identify the liveness state, which includes both moving and stationary states.

[0099] The training process of the human body recognition network in this embodiment is as follows: multiple sets of data are collected, and the data categories are divided into three types: no human body, moving human body, and stationary human body. The data are input into the human body recognition network for training, the network parameters are saved, and finally the trained human body recognition network is obtained.

[0100] S103. Perform phase correction on the raw data containing stationary human bodies and extract the phase signal.

[0101] The echo signal and the transmitted signal are separated into I / Q paths. The echo signal is mixed from RF to baseband signal, and the output baseband signal is as follows:

[0102]

[0103] The I / Q signal to be demodulated contains an added DC component. I and DC Q The DC component mainly originates from reflections from surrounding stationary objects. The reflected signal from stationary objects does not change the frequency of the detected electromagnetic wave, essentially acting as zero frequency. Therefore, there will be a noticeable DC signal in the echo signal, and the I / Q signal will appear as an ellipse on the plane. I DC Q It is the two centers of the DC bias ellipse, A I A Q These are the radii of the major and minor axes of the ellipse. θ0 is the angle of relative rotation of the ellipse due to I / Q phase imbalance, x(t) is the signal to be wound, and θ0 is the initial phase.

[0104] Due to the presence of DC bias and phase imbalance, the desired I is actually... c / Q c The signal is as follows:

[0105]

[0106] Therefore, signal correction is required before signal processing. Here, the least squares elliptic fitting algorithm is used to estimate the above parameters.

[0107] S104. Perform phase unwinding on the extracted phase signal.

[0108] The original data in this embodiment contains two signals, one real and one imaginary. The Kalman filter model, namely the state equation and the observation equation, is the key to data estimation.

[0109] Based on the motion characteristics of the chest wall displacement, the state equation is obtained as follows:

[0110]

[0111] Where, x p (k), x v (k) and x a (k) corresponds to the phase, velocity and acceleration of the phase transition of x(k), respectively; dt is the sampling interval; u(k) is the estimated value of the true phase gradient; and w(k) is the estimation error of the phase gradient.

[0112] Using the real and imaginary parts of the normalized complex signal as two observations of the phase, the observation equation is obtained as follows:

[0113]

[0114] Where v(k) is the observation error of the real and imaginary parts of the complex observation.

[0115] In this embodiment, the phase unwrapping of the extracted phase signal is implemented using a phase unwrapping algorithm based on adaptive extended Kalman filtering. Each update process in the adaptive extended Kalman filtering also updates the observation noise variance matrix R and the process noise variance matrix Q. Updating the observation noise variance matrix R and the process noise variance matrix Q includes:

[0116] A. Based on residual adaptive estimation of the observation noise variance matrix, including:

[0117] Define residual ε k+1 The difference between the actual measured value and the estimated value in step k+1 is expressed by the following formula:

[0118]

[0119] Where, H is the state observation matrix, P - E[·] represents the covariance between the true and predicted values, and E[·] represents the statistical average.

[0120] calculate By statistically averaging over time and introducing a forgetting factor (0 < α ≤ 1), the observation noise variance matrix is ​​updated using the following formula. In this embodiment, α = 0.4:

[0121]

[0122] B. Based on the adaptive estimation of the process noise variance matrix using the updated difference, including:

[0123] Define the update difference as follows:

[0124]

[0125] The process noise variance matrix is ​​updated using the following formula:

[0126] Q k+1 =αQ k+1 +(1-α)(K(k+1)r k+1 r k+1 T K(k+1) T )

[0127] In this embodiment, the update process in the adaptive extended Kalman filter is as follows:

[0128]

[0129] 1) Calculate the predicted value of the state vector. It is the estimated value from the previous moment. This is the predicted value for the moment when the entanglement is to be untangled.

[0130] 2) Calculate the estimated variance of the state vector, P. + (k) is The estimated variance matrix, P - (k+1) is The state covariance matrix.

[0131] 3) Update the observation matrix. h(x) is a phase-wound nonlinear measurement equation. Expand it using the first-order Taylor expansion, that is, find the first-order differential value of the nonlinear equation.

[0132] 4) Calculate the Kalman filter gain, where K(k+1) is the filter gain, obtained from the state covariance matrix and the observation equation.

[0133] 5) Calculate the predicted value of the measurement vector.

[0134] 6) Obtain the estimated value of the state to be untangled.

[0135] 7) Update the estimated covariance matrix, observation noise variance matrix, and process noise variance matrix.

[0136] Based on simulation data, the performance of the AEKF (Adaptive Extended Kalman Filter) algorithm in this embodiment is compared with that of the DAM algorithm, EKF (Extended Kalman Filter) algorithm, and other three unwinding methods. Figure 3 And as shown in Table 1 below:

[0137] Table 1 Comparison of the effects of three untangling methods

[0138] Model Maximum absolute error Mean Absolute Error Standard deviation DACM 1.5260 0.4452 0.2927 EKF 0.6489 0.1420 0.0312 AEKF 0.4679 0.1156 0.0203

[0139] S105. Extract the chest wall displacement-time signal from the unwound phase signal to obtain the respiratory rate of the human target.

[0140] Further, step S105 includes:

[0141] S1051. Perform a fast Fourier transform on the unwound phase signal to obtain the spectrum.

[0142] S1052. Based on the respiratory frequency range of 0.15 to 0.5 Hz, the spectrum is filtered to obtain the signal within the filtered frequency band.

[0143] S1053. Find the maximum value of the spectrum within the filtered frequency band, and calculate the number of breaths per minute, as follows:

[0144] HB=60×f b

[0145] Where HB is the number of breaths per minute, f b This represents the maximum value of the spectrum within the filtered frequency band.

[0146] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the described steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0147] Example 2:

[0148] like Figure 4 As shown, this embodiment provides a radar-based human respiratory information extraction device. The device includes an acquisition module 401, a judgment module 402, a phase correction module 403, a phase unwinding module 404, and an extraction module 405. The specific functions of each module are as follows:

[0149] The acquisition module 401 is used to mix and filter the echo signal and the transmitted signal to obtain the raw data. The transmitted signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna.

[0150] The judgment module 402 is used to process the raw data and determine whether there is a stationary human body based on the processed data;

[0151] The phase correction module 403 is used to perform phase correction on the raw data containing a stationary human body and extract the phase signal;

[0152] The phase unwinding module 404 is used to unwind the extracted phase signal.

[0153] The extraction module 405 extracts the chest wall displacement-time signal from the unwound phase signal to obtain the respiratory rate of the human target.

[0154] For the specific implementation of each of the above modules, please refer to Embodiment 1 above. It should be noted that the device provided in this embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0155] Example 3:

[0156] This embodiment provides a computer device, such as... Figure 5 As shown, it includes a processor 502, a memory, an input device 503, a display 504, and a network interface 505 connected via a device bus 501. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 506 and internal memory 507. The non-volatile storage medium 506 stores operating devices, computer programs, and a database. The internal memory 507 provides an environment for the operation of the operating devices and computer programs in the non-volatile storage medium. When the processor 502 executes the computer program stored in the memory, it implements the human respiratory information extraction method of Embodiment 1 described above, as follows:

[0157] The echo signal and the transmitted signal are mixed and filtered to obtain the original data. The transmitted signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna.

[0158] The raw data is processed, and the presence of a stationary human body is determined based on the processed data.

[0159] Phase correction is performed on the raw data containing stationary human bodies to extract the phase signal;

[0160] Phase unwrapping is performed on the extracted phase signal;

[0161] The chest wall displacement-time signal is extracted from the unwound phase signal to obtain the respiratory rate of the human target.

[0162] Example 4:

[0163] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the human respiratory information extraction method of Embodiment 1 above, as follows:

[0164] The echo signal and the transmitted signal are mixed and filtered to obtain the original data. The transmitted signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna.

[0165] The raw data is processed, and the presence of a stationary human body is determined based on the processed data.

[0166] Phase correction is performed on the raw data containing stationary human bodies to extract the phase signal;

[0167] Phase unwrapping is performed on the extracted phase signal;

[0168] The chest wall displacement-time signal is extracted from the unwound phase signal to obtain the respiratory rate of the human target.

[0169] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0170] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used or combined with an instruction execution device, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use or combined with an instruction execution device, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0171] The computer-readable storage medium described above can be used to write computer programs for executing this embodiment in one or more programming languages ​​or combinations thereof. These programming languages ​​include object-oriented programming languages—such as Java, Python, and C++—and conventional procedural programming languages—such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0172] In summary, this invention utilizes X-band radar to transmit and receive electromagnetic waves, mixes and filters the echo and transmitted signals to obtain raw data, processes this raw data to determine the state of the human body within the radar's range, identifies stationary human bodies, performs phase correction on the raw data of stationary human bodies, and then uses a phase unwinding algorithm to obtain unambiguous phase information to extract chest wall displacement-time signals, thereby obtaining the respiratory rate of the human target, resulting in better anti-interference performance. Furthermore, the phase unwinding algorithm of this invention employs an adaptive extended Kalman filter phase unwinding algorithm, which has better denoising performance compared to the extended-DACM algorithm. The adaptive algorithm can resolve the dependence of Kalman filtering on the setting of observation noise and process noise values, making the algorithm more portable and obtaining a more accurate result.

[0173] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for extracting human respiratory information based on radar, characterized by, The method comprises: mixing and filtering echo signals and transmission signals to obtain raw data, the transmission signals being radio frequency signals transmitted by an X-band radar, and the echo signals being signals received by an antenna; processing the raw data to determine whether a stationary human body exists according to the processed data; performing phase correction on the raw data in which the stationary human body exists to extract phase signals; performing phase unwrapping on the extracted phase signals; extracting a chest wall displacement-time signal from the unwrapped phase signals to obtain a breathing frequency of a human target; the processing of the raw data to determine whether a stationary human body exists according to the processed data comprises: performing short-time Fourier transform on the raw data to obtain a frequency spectrum; inputting the frequency spectrum into a trained human body recognition network to determine whether a living body exists, and if the living body exists, determining whether the living body state is in a stationary state, and if the living body state is in the stationary state, determining that a stationary human body exists; the human body recognition network is a multi-layer convolutional neural network, and the human body recognition network achieves the functions of detecting a living body and recognizing a living body state after being trained, and the living body state includes a moving state and a stationary state; the phase unwrapping on the extracted phase signals is implemented by using a phase unwrapping algorithm based on adaptive extended Kalman filtering; in each updating process of the adaptive extended Kalman filtering, an observation noise variance matrix and a process noise variance matrix are also updated, and the updating of the observation noise variance matrix and the process noise variance matrix comprises: the observation noise variance matrix is adaptively estimated based on a residual error, comprising: Definition of the residual ε k+1 represents the difference between the actual measured value and the estimated value in the k+1 step as follows: where R is the observation noise variance matrix, H is the state observation matrix, P - is the covariance between the true and predicted values, and E[·] denotes the statistical average; Computing The statistical average in time, introduce a forgetting factor 0 < α ≤ 1, using the following formula to update the observation noise variance matrix: the process noise variance matrix is adaptively estimated based on an updating difference value, comprising: an updating difference value r is defined as follows: the process noise variance matrix is updated by using the following formula: Q k+1 = aQ k+1 + (1 - a)(K(k + 1) r k+1 r k+1 T K(k + 1) T ) wherein Q is the process noise variance matrix.

2. The method of claim 1, wherein the mixing and filtering of the echo signals and the transmission signals to obtain the raw data comprises: mixing the echo signals and the transmission signals to obtain intermediate frequency signals, as follows: where τ is the time difference between the transmitted signal and the echo signal, is the phase noise of the continuous wave; performing analog-to-digital conversion on the intermediate frequency signals to obtain the raw data.

3. The method of claim 1, wherein the phase correction on the raw data in which the stationary human body exists is implemented by using a least square ellipse fitting algorithm, comprising: dividing the echo signals and the transmission signals into I / Q two paths, mixing the echo signals from radio frequency to baseband signals, and outputting the baseband signals as follows: where I / Q signals appear as an ellipse shape on a plane; DC I , DC Q is a direct current bias, i.e. the two centers of the ellipse, A I , A Q is the radius of the major axis and the minor axis of the ellipse, is the angle of I / Q phase imbalance, i.e. the relative rotation of the ellipse, x(t) is the signal to be wrapped, and θ0is the initial phase. The required I c / Q c signal is estimated using a least squares ellipse fitting algorithm c / Q c The representation of the signal I is given by 4. The method of claim 1, wherein the model of the Kalman filtering comprises a state equation and an observation equation; the state equation is obtained according to the motion characteristics of the chest wall displacement, as follows: where x p (k), x v (k), and x a (k) correspond to the phase, the speed of phase transformation, and the acceleration of phase transformation of x(k), respectively, dt is the sampling interval, u(k) is the estimated value of the real phase gradient, and w(k) is the estimation error of the phase gradient. the observation equation is obtained by taking the real part and the imaginary part of the normalized complex signal as two observation quantities of the phase, as follows: wherein v(k) is an observation error of the real part and the imaginary part of the complex observation value.

5. The method of claim 1, wherein the extraction of the chest wall displacement-time signal from the unwrapped phase signals to obtain the breathing frequency of the human target comprises: performing fast Fourier transform on the unwrapped phase signals to obtain a frequency spectrum; filtering the frequency spectrum to obtain a signal in a filter frequency band according to the range of the breathing frequency being between 0.15 and 0.5 Hz; finding a maximum value of the frequency spectrum in the filter frequency band, and calculating the number of breaths per minute, as follows: HB = 60 x f b where HB is the number of breaths per minute, f b is the maximum value of the spectrum within the filter band.

6. A radar-based human respiratory information extraction device, characterized in that, the device comprises: The acquisition module is configured to mix and filter the echo signal and a transmission signal to obtain raw data, the transmission signal being a radio frequency signal transmitted by an X-band radar, and the echo signal being a signal received by an antenna; The judgment module is configured to process the raw data and determine whether a stationary human body exists according to the processed data; The phase correction module is configured to perform phase correction on the raw data in which the stationary human body exists, and extract a phase signal; The phase unwrapping module is configured to perform phase unwrapping on the extracted phase signal; The extraction module is configured to extract a chest wall displacement-time signal from the unwrapped phase signal, and obtain a breathing frequency of the human target; The processing of the raw data and the determination of whether a stationary human body exists according to the processed data include: performing short-time Fourier transform on the raw data to obtain a frequency spectrum; inputting the frequency spectrum into a trained human body recognition network to determine whether a living body exists, and if the living body exists, determining whether a living body state is in a stationary state, and if the living body state is in the stationary state, determining that a stationary human body exists; The human body recognition network is a multi-layer convolutional neural network, and the human body recognition network achieves the functions of detecting a living body and recognizing a living body state after being trained, and the living body state includes a motion state and a stationary state; The phase unwrapping of the extracted phase signal is implemented by using a phase unwrapping algorithm based on adaptive extended Kalman filtering; In each update process of the adaptive extended Kalman filtering, the observation noise variance matrix and the process noise variance matrix are also updated, and the updating of the observation noise variance matrix and the process noise variance matrix includes: The observation noise variance matrix is adaptively estimated based on a residual error, including: The residual error ε is defined as k+1 The residual error ε is defined as the difference between the actual and estimated values in the k+1 step as follows: where R is the observation noise variance matrix, H is the state observation matrix, P - is the covariance between the true and predicted values, and E[·] denotes the statistical average; Computing The statistical average in time, introduce a forgetting factor 0 < α ≤ 1, using the following formula to update the observation noise variance matrix: The process noise variance matrix is adaptively estimated based on an update difference, including: The update difference r is defined as follows: The process noise variance matrix is updated by using the following formula: Q k+1 = aQ k+1 + (1 - a)(K(k + 1) r k+1 r k+1 T K(k + 1) T ) wherein Q is the process noise variance matrix.

7. A computer device comprising a processor and a memory for storing a processor executable program, characterized in that, The processor executes the program stored in the memory to implement the human respiratory information extraction method of any one of claims 1-5.

8. A storage medium storing a program, characterized by comprising: The program is executed by the processor to implement the human respiratory information extraction method of any one of claims 1-5.

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