CN116602646A - Human body breathing information extraction method, device, equipment and medium based on radar - Google Patents
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Abstract
The invention discloses a radar-based human respiratory information extraction method, device, equipment and medium, wherein the method comprises the following steps: mixing and filtering an echo signal and a transmitting signal to obtain original data, wherein the transmitting signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna; processing the original data, and judging whether a static human body exists or not according to the processed data; carrying out phase correction on original data with static human body, and extracting phase signals; performing phase unwrapping on the extracted phase signal; and extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target. Compared with the prior art, the method has better anti-interference effect in extracting the respiratory information from the echo signals of the X-band radar.
Description
Technical Field
The invention relates to a radar-based human respiratory information extraction method, device, equipment and medium, and belongs to the technical field of human respiratory frequency detection.
Background
One key to detecting the respiratory rate of a human body is to determine the presence of a stationary human body and to exclude various other people from the situation, ensuring the accuracy of the measurement. The X band contains an ISM band, and the band has great research potential. The wavelength of the X-band radar is compared with that of millimeter wave Lei Dachang, which makes it advantageous to penetrate medium such as rain, snow, fog, etc. The X-band radar has the advantage of penetrating through the obstacle, so that the application of the X-band radar has stronger practicability.
The unmodulated continuous wave radar is different from the FMCW radar, and can realize positioning and determine the position of a human body and the situation of multiple persons. Wherein the exclusion of multiple people is one of the keys. The method of using the neural network is proposed herein, which is trained to achieve the effect of identifying independent stationary human bodies.
The existing contactless respiration information extraction method is mostly realized by using millimeter wave radar, and phase information is obtained through unwrapping a human body echo signal. However, the resolution of the X-band radar is lower than that of the millimeter wave radar, the recognition effect on the movement of chest wall displacement is poorer, and the noise has a great influence on the phase information. The unwrapping method now commonly used is the extended-DACM algorithm, which uses two approximation processes, one using forward differential instead of differential and the second using rectangular summation instead of approximation integral. The two approximations make DACM only suitable for high sampling rate, if the sampling rate used is lower, a great error can appear, and the phase is solved by the forward difference in the cumulative summation method, but the cumulative summation process can cause the accumulation of noise, and the demodulation effect is greatly affected by noise for the signal with low signal-to-noise ratio.
Therefore, the prior art is suitable for the radar with high frequency and high sampling rate, and has poor effect of extracting respiratory information for the radar with low working frequency and sampling rate.
Disclosure of Invention
In view of the above, the invention provides a method, a device, a computer device and a storage medium for extracting human respiratory information based on radar, which have better anti-interference effect in extracting respiratory information from echo signals of an X-band radar compared with the prior art.
A first object of the present invention is to provide a radar-based human respiratory information extraction method
A second object of the present invention is to provide a radar-based human respiratory information extraction apparatus.
A third object of the present invention is to provide a computer device.
A fourth object of the present invention is to provide a storage medium.
The first object of the present invention can be achieved by adopting the following technical scheme:
a radar-based human breath information extraction method, the method comprising:
mixing and filtering an echo signal and a transmitting signal to obtain original data, wherein the transmitting signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna;
processing the original data, and judging whether a static human body exists or not according to the processed data;
carrying out phase correction on original data with static human body, and extracting phase signals;
performing phase unwrapping on the extracted phase signal;
and extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target.
Further, the mixing and filtering the echo signal and the transmitting signal to obtain the original data includes:
mixing the echo signal and the transmitting signal to obtain an intermediate frequency signal, wherein the intermediate frequency signal is represented by the following formula:
where τ is the time difference between the transmit signal and the echo signal,is the phase noise of the continuous wave;
and carrying out analog-to-digital conversion on the intermediate frequency signal to obtain original data.
Further, the processing the original data, and judging whether a static human body exists according to the processed data includes:
performing short-time Fourier transform on the original data to obtain a frequency spectrum;
inputting the frequency spectrum into a trained human body identification network, judging whether a living body exists or not, if so, judging whether the living body state is in a static state, and if so, judging that a static human body exists;
the human body recognition network is a multilayer convolutional neural network, and after training, the human body recognition network achieves the functions of detecting living bodies and recognizing living body states, wherein the living body states comprise a motion state and a static state.
Further, the phase correction of the original data with the static human body is realized by adopting a least square method ellipse fitting algorithm, which comprises the following steps:
dividing echo signals and transmitting signals into two paths of I/Q, mixing the echo signals from radio frequency to baseband signals, and outputting the baseband signals as follows:
wherein the I/Q signal appears in the shape of an ellipse on a plane; DC (direct current) I 、DC Q Is DC offset, i.e. two centers of ellipse, A I 、A Q Is the radius of the major and minor axes of the ellipse,is the I/Q phase imbalance, i.e. the angle of elliptical relative rotation, x (t) is the signal to be wound, θ 0 Is the initial phase;
adopting a least square method ellipse fitting algorithm to estimate and obtain the required I c /Q c Signal, I c /Q c The representation of the signal is as follows:
furthermore, the phase unwrapping of the extracted phase signal is realized by adopting a phase unwrapping algorithm based on adaptive extended Kalman filtering;
each updating process in the adaptive extended kalman filtering also updates the observed noise variance matrix and the process noise variance matrix, and the updating of the observed noise variance matrix and the process noise variance matrix comprises the following steps:
estimating an observed noise variance matrix based on residual adaptation, comprising:
defining residual epsilon k+1 Representing the difference between the actual measured value and the estimated value in the k+1 step, the following formula is:
wherein R is an observation noise variance matrix, H is a state observation matrix, P-is covariance between a true value and a predicted value, and E [ cn ] represents statistical average;
calculation ofIntroducing a forgetting factor 0 < alpha less than or equal to 1 by statistical averaging in time, and updating an observed noise variance matrix by adopting the following steps:
an adaptive estimation process noise variance matrix based on updated differences, comprising:
an update difference r is defined as follows:
the noise variance matrix is updated by the following steps:
Q k+1 ＝αQ k+1 +(1-α)(K(k+1)r k+1 r k+1 T K(k+1) T )
where Q is the process noise variance matrix.
Further, the Kalman filtering model comprises a state equation and an observation equation;
and obtaining a state equation according to the motion characteristics of chest wall displacement, wherein the state equation is as follows:
wherein x is p (k)、x v (k) And x a (k) The phase of x (k), the speed of phase transformation and the acceleration of phase transformation are respectively corresponding, dt is the sampling interval, u (k) is the estimated value of the true phase gradient, and w (k) is the estimated error of the phase gradient;
taking the real part and the imaginary part of the normalized complex signal as two observables of the phase to obtain an observation equation, wherein the observation equation is as follows:
where v (k) is the observation error of the real and imaginary parts of the complex observations.
Further, the extracting chest wall displacement-time signal from the unwrapped phase signal to obtain the respiratory rate of the human target includes:
performing fast Fourier transform on the unwrapped phase signals to obtain a frequency spectrum;
filtering the frequency spectrum according to the range of the breathing frequency between 0.15 and 0.5Hz to obtain signals in a filtering frequency band; finding the maximum value of the frequency spectrum in the filtering frequency band, and calculating the number of times of breathing per minute, wherein the following formula is as follows:
HB＝60×f b
wherein HB is the number of breaths per minute, f b Is the maximum of the spectrum within the filtered band.
The second object of the invention can be achieved by adopting the following technical scheme:
a radar-based human breath information extraction apparatus, the apparatus comprising:
the acquisition module is used for carrying out frequency mixing and filtering on echo signals and transmitting signals to obtain original data, wherein the transmitting signals are radio frequency signals transmitted by an X-band radar, and the echo signals are signals received by an antenna;
the judging module is used for processing the original data and judging whether a static human body exists or not according to the processed data;
the phase correction module is used for carrying out phase correction on the original data with the static human body and extracting phase signals;
the phase unwrapping module is used for phase unwrapping the extracted phase signals;
and the extraction module is used for extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target.
The third object of the present invention can be achieved by adopting the following technical scheme:
the computer equipment comprises a processor and a memory for storing a program executable by the processor, and is characterized in that the human body breathing information extraction method is realized when the processor executes the program stored by the memory.
The fourth object of the present invention can be achieved by adopting the following technical scheme:
a storage medium storing a program which, when executed by a processor, implements the human respiratory information extraction method described above.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the X-band radar transmits electromagnetic waves, receives the electromagnetic waves, mixes and filters echo signals and transmitting signals to obtain original data, processes the original data to judge the human body state in the radar action range, identifies a static human body, realizes phase correction on the original data of the static human body, and then uses a phase unwrapping algorithm to obtain non-fuzzy phase information so as to extract chest wall displacement-time signals, thereby obtaining the respiratory frequency of a human body target and having a better anti-interference effect.
2. Compared with the extension-DACM algorithm, the phase unwrapping algorithm of the self-adaptive extended Kalman filter has better denoising effect, and the self-adaptive algorithm can solve the dependence of the Kalman filter on the setting of observation noise and process noise values, so that the portability of the algorithm is stronger, and a more accurate result is obtained.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a radar-based human respiratory information extraction method according to embodiment 1 of the present invention.
Fig. 2 is a block diagram of a human body recognition network according to embodiment 1 of the present invention.
Fig. 3 is a diagram showing simulation results of the adaptive extended kalman filter algorithm of embodiment 1 of the present invention compared with the existing DACM algorithm, EKF algorithm, and true values.
Fig. 4 is a block diagram showing the structure of a radar-based human respiratory information extraction apparatus according to embodiment 2 of the present invention.
Fig. 5 is a block diagram showing the structure of a computer device according to embodiment 3 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Example 1:
as shown in fig. 1, the present embodiment provides a radar-based human respiratory information extraction method, which includes the following steps:
s101, mixing and filtering the echo signals and the transmitting signals to obtain original data.
In the embodiment, USRP B210 is used for constructing a continuous wave radar system working at 5.8GHz, the system is a single-shot single-receiving radar system, an antenna is a narrow-band antenna working near 5.8GHz, when static human body data is acquired, the distance between a human body and the antenna is 0.6m, and the chest is opposite to the antenna; various activity state data of a human body are collected, and the human body moves within the range of 2 meters of the radar.
The embodiment adopts an X-band radar to transmit radio frequency signals, specifically transmits unmodulated continuous wave signals, takes the signals as transmitting signals, and has the following formula:
an echo signal is received by an antenna, and the echo signal has the following formula:
where t is time, τ is the time difference between the transmitted signal and the received signal,in this embodiment of continuous wave phase noise, mixing and filtering the echo signal and the transmit signal to obtain the original data includes:
s1011, mixing the echo signal and the transmitting signal to obtain an intermediate frequency signal, wherein the intermediate frequency signal is represented by the following formula:
s1012, performing analog-to-digital conversion on the intermediate frequency signal to obtain original data.
S102, processing the original data, and judging whether a static human body exists or not according to the processed data.
Further, the step S102 includes:
s1021, performing short-time Fourier transform on the original data to obtain a time-Doppler spectrum serving as a frequency spectrum.
S1022, inputting the frequency spectrum into the trained human body identification network, judging whether a living body exists, if so, judging whether the living body state is in a static state, and if so, judging that a static human body exists.
The human body recognition network in this embodiment is a three-layer convolutional neural network, as shown in fig. 2, and after the human body recognition network is trained, the human body recognition network achieves the functions of detecting a living body and recognizing a living body state, wherein the living body state includes a motion state and a static state.
The human body recognition network training process of the embodiment is as follows: and acquiring multiple groups of data, wherein the data types are classified into unmanned human body, moving human body and static human body, inputting the data into a human body identification network for training, and storing network parameters to finally obtain the trained human body identification network.
S103, carrying out phase correction on the original data with the static human body, and extracting a phase signal.
The echo signal and the transmitting signal are divided into two paths of I/Q, the echo signal is mixed from radio frequency to baseband signal, and the output baseband signal is as follows:
wherein an additive DC component DC is present in the I/Q signal to be demodulated I And DC Q The direct current component mainly sources the reflection of surrounding stationary objects, the frequency of the detected electromagnetic wave is not changed by the stationary object reflection signal, which is equivalent to 0 frequency, so that an obvious direct current signal exists in the echo signal, and the I/Q signal is in an elliptical shape on a plane; DC (direct current) I 、DC Q Is DC offset, i.e. two centers of ellipse, A I 、A Q Is the radius of the major and minor axes of the ellipse,is the I/Q phase imbalance, i.e. the angle of elliptical relative rotation, x (t) is the signal to be wound, θ 0 Is the initial phase.
The actual expected I is due to the existence of direct current bias and phase unbalance c /Q c The signal is as follows:
signal correction is required prior to signal processing, where the above parameters are estimated using a least squares elliptic fitting algorithm.
S104, performing phase unwrapping on the extracted phase signals.
The original data of the embodiment includes two paths of signals of a real part and an imaginary part, and a Kalman filtering model, namely a state equation and an observation equation, is a key of data estimation.
And obtaining a state equation according to the motion characteristics of chest wall displacement, wherein the state equation is as follows:
wherein x is p (k)、x v (k) And x a (k) The phase, the speed of the phase transformation and the acceleration of the phase transformation, respectively, corresponding to x (k), dt being the sampling interval, u (k) being the estimated value of the true phase gradient, w (k) being the phase gradientIs determined by the estimation error of (a);
taking the real part and the imaginary part of the normalized complex signal as two observables of the phase to obtain an observation equation, wherein the observation equation is as follows:
where v (k) is the observation error of the real and imaginary parts of the complex observations.
In this embodiment, phase unwrapping of the extracted phase signal is implemented by using a phase unwrapping algorithm based on adaptive extended kalman filtering, and each updating process in the adaptive extended kalman filtering also updates the observed noise variance matrix R and the process noise variance matrix Q, where updating the observed noise variance matrix R and the process noise variance matrix Q includes:
A. estimating an observed noise variance matrix based on residual adaptation, comprising:
defining residual epsilon k+1 Representing the difference between the actual measured value and the estimated value in the k+1 step, the following formula is:
wherein H is a state observation matrix, P - E [. Cndot.]Representing a statistical average.
Calculation ofA forgetting factor of 0 < alpha is introduced to be less than or equal to 1 by statistical averaging in time, and the observed noise variance matrix is updated by adopting the following steps, wherein alpha=0.4 is set in the embodiment:
B. an adaptive estimation process noise variance matrix based on updated differences, comprising:
defining an updated difference value as follows:
the noise variance matrix is updated by the following steps:
Q k+1 ＝αQ k+1 +(1-α)(K(k+1)r k+1 r k+1 T K(k+1) T )
in this embodiment, each update procedure in the adaptive extended kalman filter is as follows:
1) A predicted value of the state vector is calculated,is an estimate of the previous moment, +.>The predicted value is the time of the to-be-unwrapped winding.
2) Calculating the estimated variance of the state vector, P+ (k) isIs an estimated variance matrix, P - (k+1) is->State covariance matrix of (a).
3) Updating the observation matrix, wherein h (x) is a phase-wound nonlinear measurement equation, and the first-order differential value is obtained for the nonlinear equation by using a first-order Taylor expansion type expansion.
4) The Kalman filter gain is calculated, K (k+1) is the filter gain, and is obtained by the state covariance matrix and the observation equation.
5) A predicted value of the measurement vector is calculated.
6) And obtaining an estimated value of the state to be unwrapped.
7) Updating the estimated covariance matrix, the observed noise variance matrix and the process noise variance matrix.
Based on simulation data, the AEKF (adaptive extended kalman filter) algorithm of the present embodiment is compared with the DACM algorithm, the EKF (extended kalman filter) algorithm, and the effects of the three unwrapping methods, as shown in fig. 3 and table 1 below:
table 1 effect comparison of three disentanglement methods
| Model | Maximum absolute value error | Average absolute error | Standard deviation of |
| DACM | 1.5260 | 0.4452 | 0.2927 |
| EKF | 0.6489 | 0.1420 | 0.0312 |
| AEKF | 0.4679 | 0.1156 | 0.0203 |
S105, extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory rate of the human body target.
Further, the step S105 includes:
s1051, performing fast Fourier transform on the unwrapped phase signals to obtain a frequency spectrum.
S1052, filtering the frequency spectrum according to the range of the breathing frequency between 0.15 and 0.5Hz to obtain signals in a filtering frequency band.
S1053, finding the maximum value of the frequency spectrum in the filtering frequency band, and calculating the number of times of breathing per minute, wherein the following formula is shown in the specification:
HB＝60×f b
wherein HB is the number of breaths per minute, f b Is the maximum of the spectrum within the filtered band.
It should be noted that while the method operations of the above embodiments are described in a particular order, this does not require or imply that the operations must be performed in that particular order or that all of the illustrated operations be performed in order to achieve desirable results. Rather, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
Example 2:
as shown in fig. 4, the embodiment provides a radar-based human respiratory information extraction device, which includes an acquisition module 401, a judgment module 402, a phase correction module 403, a phase unwrapping module 404 and an extraction module 405, where specific functions of the modules are as follows:
the acquisition module 401 is configured to mix and filter an echo signal and a transmitting signal, to obtain original data, where the transmitting signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna;
a judging module 402, configured to process the original data, and judge whether a static human body exists according to the processed data;
a phase correction module 403, configured to perform phase correction on original data with a stationary human body, and extract a phase signal;
a phase unwrapping module 404, configured to phase unwrap the extracted phase signal;
the extraction module 405 extracts chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory rate of the human target.
Specific implementation of each module described above is referred to above in embodiment 1; it should be noted that, the apparatus provided in this embodiment is only exemplified by the division of the above functional modules, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure is divided into different functional modules, so as to perform all or part of the functions described above.
Example 3:
the present embodiment provides a computer apparatus, as shown in fig. 5, which includes a processor 502, a memory, an input device 503, a display 504 and a network interface 505 connected through a device bus 501, where the processor is used to provide computing and control capabilities, the memory includes a nonvolatile storage medium 506 and an internal memory 507, the nonvolatile storage medium 506 stores an operating device, a computer program and a database, the internal memory 507 provides an environment for the operation of the operating device and the computer program in the nonvolatile storage medium, and when the processor 502 executes the computer program stored in the memory, the human breathing information extraction method of the above embodiment 1 is implemented as follows:
mixing and filtering an echo signal and a transmitting signal to obtain original data, wherein the transmitting signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna;
processing the original data, and judging whether a static human body exists or not according to the processed data;
carrying out phase correction on original data with static human body, and extracting phase signals;
performing phase unwrapping on the extracted phase signal;
and extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target.
Example 4:
the present embodiment provides a storage medium, which is a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method for extracting human respiratory information according to embodiment 1 is implemented as follows:
mixing and filtering an echo signal and a transmitting signal to obtain original data, wherein the transmitting signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna;
processing the original data, and judging whether a static human body exists or not according to the processed data;
carrying out phase correction on original data with static human body, and extracting phase signals;
performing phase unwrapping on the extracted phase signal;
and extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target.
The computer readable storage medium of the present embodiment may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an apparatus, device, or means of electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In this embodiment, the computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In the present embodiment, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. A computer program embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable storage medium may be written in one or more programming languages, including an object oriented programming language such as Java, python, C ++ and conventional procedural programming languages, such as the C-language or similar programming languages, or combinations thereof for performing the present embodiments. The program may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
In summary, the method transmits electromagnetic waves through the X-band radar, receives the electromagnetic waves, mixes and filters echo signals and transmitting signals to obtain original data, processes the original data to judge the human body state in the radar action range, identifies a static human body, realizes phase correction on the original data of the static human body, and then uses a phase unwrapping algorithm to obtain non-fuzzy phase information so as to extract chest wall displacement-time signals, thereby obtaining the respiratory frequency of a human body target and having better anti-interference effect; in addition, the phase unwrapping algorithm of the invention uses the phase unwrapping algorithm of the self-adaptive extended Kalman filter, has better denoising effect compared with the extended-DACM algorithm, and the self-adaptive algorithm can solve the dependence of the Kalman filter on the setting of observation noise and process noise values, so that the portability of the algorithm is stronger, and a more accurate result is obtained.
The above description is only of the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive conception of the present invention equally within the scope of the disclosure of the present invention.
Claims (10)
1. A radar-based human respiratory information extraction method, the method comprising:
mixing and filtering an echo signal and a transmitting signal to obtain original data, wherein the transmitting signal is a radio frequency signal transmitted by an X-band radar, and the echo signal is a signal received by an antenna;
processing the original data, and judging whether a static human body exists or not according to the processed data;
carrying out phase correction on original data with static human body, and extracting phase signals;
performing phase unwrapping on the extracted phase signal;
and extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target.
2. The method for extracting respiratory information of a human body according to claim 1, wherein the mixing and filtering the echo signal and the transmission signal to obtain the original data comprises:
mixing the echo signal and the transmitting signal to obtain an intermediate frequency signal, wherein the intermediate frequency signal is represented by the following formula:
where τ is the time difference between the transmit signal and the echo signal,is the phase noise of the continuous wave;
and carrying out analog-to-digital conversion on the intermediate frequency signal to obtain original data.
3. The method for extracting respiratory information of a human body according to claim 1, wherein the processing the raw data and determining whether a stationary human body exists according to the processed data comprises:
performing short-time Fourier transform on the original data to obtain a frequency spectrum;
inputting the frequency spectrum into a trained human body identification network, judging whether a living body exists or not, if so, judging whether the living body state is in a static state, and if so, judging that a static human body exists;
the human body recognition network is a multilayer convolutional neural network, and after training, the human body recognition network achieves the functions of detecting living bodies and recognizing living body states, wherein the living body states comprise a motion state and a static state.
4. The method for extracting respiratory information of a human body according to claim 1, wherein the phase correction of the original data of the presence of the stationary human body is implemented by a least square ellipse fitting algorithm, comprising:
dividing echo signals and transmitting signals into two paths of I/Q, mixing the echo signals from radio frequency to baseband signals, and outputting the baseband signals as follows:
wherein the I/Q signal appears in the shape of an ellipse on a plane; DC (direct current) I 、DC Q Is DC offset, i.e. two centers of ellipse, A I 、A Q Is the radius of the major and minor axes of the ellipse,is the I/Q phase imbalance, i.e. the angle of elliptical relative rotation, x (t) is the signal to be wound, θ 0 Is the initial phase;
adopting a least square method ellipse fitting algorithm to estimate and obtain the required I c /Q c Signal, I c /Q c The representation of the signal is as follows:
5. the method for extracting respiratory information of a human body according to claim 1, wherein the phase unwrapping of the extracted phase signal is implemented by a phase unwrapping algorithm based on adaptive extended kalman filtering;
each updating process in the adaptive extended kalman filtering also updates the observed noise variance matrix and the process noise variance matrix, and the updating of the observed noise variance matrix and the process noise variance matrix comprises the following steps:
estimating an observed noise variance matrix based on residual adaptation, comprising:
defining residual epsilon k+1 Representing the difference between the actual measured value and the estimated value in the k+1 step, the following formula is:
wherein R is an observation noise variance matrix, H is a state observation matrix, and P - E [. Cndot.]Representing a statistical average;
calculation ofIntroducing a forgetting factor 0 < alpha less than or equal to 1 by statistical averaging in time, and updating an observed noise variance matrix by adopting the following steps:
an adaptive estimation process noise variance matrix based on updated differences, comprising:
an update difference r is defined as follows:
the noise variance matrix is updated by the following steps:
Q k+1 ＝αQ k+1 +(1-α)(K(k+1)r k+1 r k+1 T K(k+1) T )
where Q is the process noise variance matrix.
6. The method according to claim 5, wherein the kalman filter model includes a state equation and an observation equation;
and obtaining a state equation according to the motion characteristics of chest wall displacement, wherein the state equation is as follows:
wherein x is p (k)、x v (k) And x a (k) The phase of x (k), the speed of phase transformation and the acceleration of phase transformation are respectively corresponding, dt is the sampling interval, u (k) is the estimated value of the true phase gradient, and w (k) is the estimated error of the phase gradient;
taking the real part and the imaginary part of the normalized complex signal as two observables of the phase to obtain an observation equation, wherein the observation equation is as follows:
where v (k) is the observation error of the real and imaginary parts of the complex observations.
7. The method for extracting respiratory information of a human body according to claim 1, wherein the step of extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory rate of the human body target comprises:
performing fast Fourier transform on the unwrapped phase signals to obtain a frequency spectrum;
filtering the frequency spectrum according to the range of the breathing frequency between 0.15 and 0.5Hz to obtain signals in a filtering frequency band;
finding the maximum value of the frequency spectrum in the filtering frequency band, and calculating the number of times of breathing per minute, wherein the following formula is as follows:
HB＝60×f b
wherein HB is the number of breaths per minute, f b Is the maximum of the spectrum within the filtered band.
8. A radar-based human breath information extraction apparatus, the apparatus comprising:
the acquisition module is used for carrying out frequency mixing and filtering on echo signals and transmitting signals to obtain original data, wherein the transmitting signals are radio frequency signals transmitted by an X-band radar, and the echo signals are signals received by an antenna;
the judging module is used for processing the original data and judging whether a static human body exists or not according to the processed data;
the phase correction module is used for carrying out phase correction on the original data with the static human body and extracting phase signals;
the phase unwrapping module is used for phase unwrapping the extracted phase signals;
and the extraction module is used for extracting chest wall displacement-time signals from the unwrapped phase signals to obtain the respiratory frequency of the human body target.
9. A computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the method of extracting human respiratory information according to any one of claims 1-7.
10. A storage medium storing a program, wherein the program, when executed by a processor, implements the human respiratory information extraction method according to any one of claims 1 to 7.
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| CN117148309B (en) * | 2023-11-01 | 2024-01-30 | 德心智能科技(常州)有限公司 | Millimeter wave radar human body sensing method and system applied to community grid inspection |
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