CN113576461B - A method for identifying weak human features using life detection radar - Google Patents
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Abstract
本发明公开了一种生命探测雷达微弱人体特征辨识方法，该方法包括获取雷达回波信号，其中，雷达回波信号包括心跳信号、呼吸信号；对所述雷达回波信号进行分解得到M个IMF分量；分别计算每个IMF分量的能量，并分析得到第一回波能量信号、第二回波能量信号；分别对所述第一回波能量信号、所述第二回波能量信号进行PCA降噪处理得到第一降噪回波信号、第二降噪回波信号；分别对所述第一降噪回波信号、所述第二降噪回波信号进行自相关处理提取得到心跳信号、呼吸信号。本发明可以从体动信号中分离出心跳信号，从该信号中提取的心率值和从心电信号中提取的心率值具有很强的相关性。
The present invention discloses a method for identifying weak human features of a life detection radar, the method comprising acquiring a radar echo signal, wherein the radar echo signal comprises a heartbeat signal and a breathing signal; decomposing the radar echo signal to obtain M IMF components; calculating the energy of each IMF component respectively, and analyzing to obtain a first echo energy signal and a second echo energy signal; performing PCA denoising processing on the first echo energy signal and the second echo energy signal respectively to obtain a first denoised echo signal and a second denoised echo signal; performing autocorrelation processing on the first denoised echo signal and the second denoised echo signal respectively to extract a heartbeat signal and a breathing signal. The present invention can separate a heartbeat signal from a body motion signal, and the heart rate value extracted from the signal has a strong correlation with the heart rate value extracted from an electrocardiogram signal.
Description
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a weak human body characteristic identification method of a life detection radar.
Background
The biological radar uses electromagnetic waves as media, transmits electromagnetic pulses, receives echo signals after being reflected by a human body, and processes and analyzes the echo signals to obtain physiological information of a living body. The biological radar solves the problems that laser and infrared detection are seriously affected by temperature and are blocked by objects to fail, and also solves the problems that the ultrasonic detection space is large in propagation attenuation and is interfered by reflection of environmental sundries and water, ice and soil are blocked to fail. The technology can be widely applied to the fields of military medicine, disaster medicine, urban anti-terrorism and the like, and is a very important leading edge technical field recognized by the international science and technology community.
Compared with the traditional contact type monitoring, the non-contact type monitoring method can reduce uncomfortable feeling of the electrode, the lead and the sensor on the monitored target, can overcome the monitoring of patients which are difficult to use the traditional contact type monitoring method, and can also avoid the influence that the contact type device possibly falls off to cause the monitoring to be problematic. Because the biological radar has the characteristics, the biological radar can be used for realizing non-contact detection of vital signs of human bodies by penetrating through a medium with a certain thickness at a certain distance. Vital sign signals consist of various parameters such as respiration, heartbeat, blood pressure, blood oxygen concentration and the like, and the signals are usually very weak and are very easy to submerge in a strong clutter background. Respiration and heartbeat signals, respiration can cause chest to do a negative motion, so that positive and negative Doppler frequency domains are generated, the positive and negative Doppler frequency domains are received by a receiver after being modulated with high-frequency signals emitted by a radar, the receiver can extract respiration signals after carrying out filtering operation, the heartbeat signals are the same, and a plurality of harmonic waves with different frequencies and distributed near zero frequency can be generated due to multi-point reflection of the chest and uneven respiration frequency. The data acquisition and analysis show that the life signal is a signal with low signal-to-noise ratio, the frequency is extremely low, the quasi-period and the multi-harmonic is composed, and the clutter signal can be similar to Gaussian colored noise, so that the signal processing of the biological radar can simplify the processing of extracting the signal from a harmonic model interfered by the Gaussian colored noise, and the clutter suppression and the signal-to-noise ratio improvement. The biological radar can be used for non-contact detection of life signals (respiration, body movement and the like) of human bodies, is widely applied to occasions such as families, community hospitals and the like, and is used for non-contact detection of the existing biological radar such as radar detection through blood pressure and radar detection through respiration signals. The radar detection method for the blood pressure comprises the steps of collecting an aortic pulse waveform signal of a subject by using a radar, extracting characteristic points of the aortic pulse waveform signal to obtain all main characteristic points, determining a mapping relation between a time period of the aortic pulse and all the characteristic points, solving pulse wave conduction time according to the mapping relation by using two characteristic points of a first contraction peak of the aortic pulse and a stopping point of a ejection period, namely a trough of a dicrotic wave, solving a corresponding blood pressure value according to the conduction time, further realizing detection of a human body vital signal according to the detected blood pressure value, wherein the respiratory signal can be easily detected by using the radar by using the respiratory signal, and a digital filter with a simple cut-off frequency of 0.8Hz can be extracted by sampling, and further realizing detection of the human body vital signal according to the detected respiratory signal.
However, in practical application, the heartbeat signal of the human body needs to be detected, so that the life condition of the human body can be reflected better, but the heartbeat signal is detected by directly adopting a band-pass filter (the lower limit cut-off frequency is 0.8Hz, and the upper limit cut-off frequency is 3 Hz), and the higher harmonic wave of the respiratory signal and the environmental Gaussian noise can be detected, so that the heartbeat signal is difficult to extract.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a weak human body characteristic identification method of a life detection radar.
One embodiment of the invention provides a weak human body feature identification method of a life detection radar, which comprises the following steps:
Acquiring radar echo signals, wherein the radar echo signals comprise heartbeat signals and breathing signals;
decomposing the radar echo signals to obtain M IMF components;
respectively calculating the energy of each IMF component, and analyzing to obtain a first echo energy signal and a second echo energy signal;
PCA noise reduction processing is carried out on the first echo energy signal and the second echo energy signal respectively to obtain a first noise reduction echo signal and a second noise reduction echo signal;
And respectively carrying out autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal to extract a heartbeat signal and a respiratory signal.
In one embodiment of the invention, the acquired radar echo signal is expressed as:
Wherein, β0＝2kr0,Lambda is the emission wavelength, d is the wall thickness, epsilon is the dielectric constant, omega 1＝2πf1,ω2＝2πf2,f1 and f 2 represent the frequency of respiration and heartbeat, respectively, delta 1 and delta 2 represent the amplitude of respiration and heartbeat, respectively, phi 2 is the constant phase.
In one embodiment of the present invention, decomposing the radar echo signal to obtain M IMF components includes:
And decomposing the radar echo signals by utilizing CEEMDAN algorithm to obtain the M IMF components.
In one embodiment of the present invention, separately calculating the energy of each IMF component and analyzing the first and second echo energy signals includes:
calculating the energy of each IMF component to obtain an IMF energy vector;
Normalizing the IMF energy vector to obtain a normalized weight vector;
the first and second echo energy signals are determined from the M IMF components according to a normalized weight vector.
In an embodiment of the present invention, performing PCA noise reduction processing on the first echo energy signal and the second echo energy signal to obtain a first noise reduction echo signal and a second noise reduction echo signal respectively further includes:
Adding noise to the first echo energy signal and the second echo energy signal to obtain a first noise-carrying echo energy signal and a second noise-carrying echo energy signal;
PCA noise reduction processing is respectively carried out on the first noise-carrying echo energy signal and the second noise-carrying echo energy signal to obtain the first noise-reducing echo signal and the second noise-reducing echo signal.
In one embodiment of the present invention, performing autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal to obtain a heartbeat signal and a respiratory signal respectively includes:
And respectively carrying out multiple autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal to extract a heartbeat signal and a respiratory signal.
In one embodiment of the present invention, the frequency range of the heartbeat signal is 0.8hz to 3.0hz.
In one embodiment of the present invention, the respiratory signal frequency range is 0.05hz to 0.8hz.
Compared with the prior art, the invention has the beneficial effects that:
The weak human body characteristic identification method of the life detection radar provided by the invention can separate the heartbeat signal from the body movement signal, and the heart rate value extracted from the signal and the heart rate value extracted from the electrocardiosignal have strong correlation.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a schematic flow chart of a method for identifying weak human body characteristics of a life detection radar according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of principal component analysis of PCA provided by an embodiment of the present invention;
Fig. 3 (a) -3 (b) are schematic diagrams of a radar echo signal containing noise and a radar echo signal after denoising according to an embodiment of the present invention;
Fig. 4 is a schematic diagram of a radar echo signal time domain analysis result provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of an FFT image of a radar echo signal at different IMF components provided by an embodiment of the present invention;
Fig. 6 (a) -6 (b) are schematic diagrams of simulation results of respiratory signals and heartbeat signals under a fixed respiratory frequency and a fixed heartbeat frequency according to an embodiment of the present invention;
Fig. 7 (a) -7 (b) are schematic diagrams of simulation results of respiratory signals and heartbeat signals under a fixed respiratory frequency and a variable heartbeat frequency according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a weak human body feature recognition device of a life detection radar according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but embodiments of the present invention are not limited thereto.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a method for identifying weak human body features of a life detection radar according to an embodiment of the present invention. The embodiment provides a weak human body characteristic identification method of a life detection radar, which comprises the following steps:
s1, acquiring radar echo signals, wherein the radar echo signals comprise heartbeat signals and breathing signals.
Specifically, the present embodiment provides that the radar emits a single-frequency continuous sine wave, and the complex signal thereof is expressed as:
where U 0 represents the complex signal amplitude and ω 0 represents the complex signal phase. The distance between the target and the radar in this embodiment can be expressed as:
r(t)=r0+Δr(t) (2)
Where r 0 denotes the distance between the living target and the radar (including the thickness of the wall), and Δr (t) denotes the distance change caused by the target respiration and heartbeat. The echo signal received by the radar is expressed as:
Where μ represents the attenuation factor (including the two-way attenuation in free space and through the wall), a represents the amplitude modulation of the echo by the target, β 0 and 2kΔr (t) represent the phase shift, β 0＝2kr0, Lambda is the emission wavelength.
Since the propagation speed of electromagnetic waves in different media is inversely proportional to the square root of the dielectric constant of the media, extra time delay is generated when the electromagnetic waves penetrate the wall, and an additional phase difference is caused, and if the thickness of the wall is d and the dielectric constant is epsilon, the formula (3) can be expressed again as:
Wherein, Breathing and heartbeat can be generally regarded as simple harmonic vibrations, and the range fluctuation of radar and life targets caused by the simple harmonic vibrations is expressed as follows:
Δr(t)=Δ1sin(ω1t)+Δ2sin(ω2t+φ2) (5)
Wherein ω 1＝2πf1,ω2＝2πf2,f1 and f 2 represent the frequency of respiration and heartbeat, respectively, Δ 1 and Δ 2 represent the amplitude of respiration and heartbeat, respectively, and Φ 2 is a constant phase. The Doppler signal is reflected in the phase of the echo, the carrier frequency is removed by adopting a quadrature phase detection method in the embodiment, and the zero intermediate frequency signal is obtained and expressed as:
Performing normalization processing on the formula (4) through the formula (6), and obtaining a normalized signal expressed as:
Ss(t)＝exp{-jφ0-j2k[Δ1 sin(ω1t)+Δ2sin(ω2t+φ2)]} (7)
since the phase contains a sine expression, in order to facilitate analysis of the relationship between the sine fundamental wave and each harmonic, the present embodiment uses the bessel function to express:
then the expansion of equation (7) using equation (8) is expressed as:
Wherein m1 and m2 are integers. As can be seen from the formula (9), the spectrum of the radar echo signal of the present embodiment includes 3 parts:
(1) m1=m2=0 is a constant component of (a);
(2) Fundamental angular frequencies ω 1 and ω 2 of respiration and heartbeat;
(3) The combination of respiratory and heartbeat angular frequency harmonics m 1ω1+m2ω2.
S2, decomposing the radar echo signals to obtain M IMF components.
Specifically, the method for decomposing the radar echo signal to obtain M content modal components (INTRINSIC MODE FUNCTIONS, IMF components for short) comprises the step of decomposing the radar echo signal by using a Complete EEMD WITH ADAPTIVE Noise (CEEMDAN) of adaptive Noise to obtain M IMF components. Specifically:
Since the respiration signal and the heartbeat signal are both present in the echo signal, the noise of the small signal (heartbeat signal) is easily removed as a large signal (respiration signal) when the noise is reduced, so we should separate the respiration and heartbeat signals first. According to the difference of the breathing signal and the heartbeat signal frequency, the embodiment utilizes CEEMDAN algorithm to decompose the radar echo signal obtained by S1 into a plurality of IMF components according to the signal peak value and the corresponding frequency point, so that the two effective signals of the breathing signal and the heartbeat signal can be conveniently separated. In the embodiment, a method of adding limited times of self-adaptive white noise is adopted, and in CEEMDAN algorithm, the decomposed modal IMF component is used And (3) representing. Operator E j (·) represents the j-th modal IMF component sequence of the given signal obtained through CEEMDAN algorithm, ω i is the i-th added gaussian white noise conforming to N (0, 1), S s (t) is the radar echo signal sequence obtained in S1, the white noise is added to the radar echo signal sequence and is represented as S s(t)＝ε0ωi (t), and the CEEMDAN algorithm is used to decompose the radar echo signal sequence after adding the white noise, where the process is as follows:
First, the CEEMDAN th modal component is obtained by decomposition Then calculate the 1 st residual signalCarrying out repeated decomposition on the signal R 1(t)+ε1E1ωi (t) for N times by utilizing CEEMDAN algorithm to obtain the 2 nd modal componentFor k=2, 3, M, calculating the kth residual marginThe radar echo signal sequence decomposed by CEEMDAN algorithm is repeatedly utilized, and the k+1th modal component is calculated as:
and (3) performing multiple operations until the residual margin is not suitable for being decomposed, and ending the decomposition. The final residual margin is expressed as:
finally, the radar echo signal of the present embodiment is decomposed into M modal components IMF expressed as:
s3, respectively calculating the energy of each IMF component, and analyzing to obtain a first echo energy signal and a second echo energy signal.
Specifically, the target signal in this embodiment obtains M IMF components through CEEMDAN algorithm, where each IMF component spectrum has a corresponding center frequency, and represents a target frequency or a noise frequency. Since the target information is unknown, it is difficult to extract the target signal features from the viewpoint of signal spectrum analysis at this time. Each IMF component energy spectrum of the signal can better display energy variation of different target IMF components, so this embodiment proposes a feature extraction method based on IMF component energy ratio, and specific S3 includes S3.1, S3.2, S3.3:
S3.1, calculating the energy of each IMF component to obtain an IMF energy vector.
Specifically, the calculation of the energy E i of each IMF component in this embodiment is expressed as:
Thereby obtaining IMF energy vectors V E＝[E1,E2,…,EM corresponding to the M IMF components.
And S3.2, carrying out normalization processing on the IMF energy vector to obtain a normalized weight vector.
Specifically, in this embodiment, the IMF energy vector V E is normalized, and the obtained normalized weight vector is expressed as:
V′E＝[p1,p2,…,pM] (14)
Wherein, Representing the normalized weight of each IMF component,Representing the sum of the energies of the M IMF components.
And S3.3, determining a first echo energy signal and a second echo energy signal from the M IMF components according to the normalized weight vector.
Specifically, according to the calculation method of the formula (14), the IMF component with high energy ratio can obtain larger weight, and the IMF component with small energy ratio can only obtain smaller weight, so that the method is more beneficial to highlighting the signal main component and reducing the bandwidth influence suffered by IMF energy analysis. The difference of different sound target vibration mechanisms is analyzed by the signal high-low frequency band energy difference characteristics, so that the energy distribution characteristics of each IMF component of target radiation noise are obviously different, but due to the existence of environmental noise or measurement system noise, the measurement test signal of the target radiation noise can be affected to a certain extent, and at the moment, the target identification can not be completed by taking the IMF energy vector V E as a target characteristic parameter. Therefore, according to the characteristics of the target, the high-low frequency energy difference of the target signal is defined, and is used as a signal characteristic parameter, and the target identification and classification are realized by combining the frequency band energy difference characteristic of the target signal with the IMF energy vector V E analysis.
Assuming that the radar echo signal is decomposed to obtain M IMF components, recording that the ith IMF component has L sampling points, wherein the instantaneous frequency of the p sampling point is f i,p, the instantaneous amplitude is A i,p, and the instantaneous intensity of the p point isAccording to the frequency distribution characteristics of typical radar echo signals, defining the frequency value as a low frequency band at 50-800 Hz and a high frequency band at 800-3000 Hz. If the instantaneous intensity of the time-frequency sampling point in the low frequency band is denoted as B l1,Bl2,…,Bln and n is the number of time-frequency sampling points in the low frequency band, the total low-frequency energy P l of the radar echo signal is expressed as:
Similarly, the instantaneous intensity of the time-frequency sampling point in the high frequency band is marked as B l1,Bl2,…,Blm, and m is the number of time-frequency sampling points in the high frequency band, so that the total high-frequency energy P h of the radar echo signal is expressed as:
Then the high-low frequency energy difference of the radar echo signal is defined as:
ΔP=Ph-Pl (17)
after the energy characteristic analysis and noise reduction, two sections of signals, namely a first echo energy signal f 1 (t) with the frequency of 0.05Hz to 0.8Hz and a second echo energy signal f 2 (t) with the frequency of 0.8Hz to 3Hz, can be obtained, and the main components in the two sections of signals respectively correspond to a respiratory signal and a heartbeat signal.
S4, PCA noise reduction processing is carried out on the first echo energy signal and the second echo energy signal respectively to obtain a first noise reduction echo signal and a second noise reduction echo signal.
Specifically, since PCA principal component analysis screens principal components in a signal, the use of PCA is highly likely to filter out a useful signal as noise when the signal-to-noise ratio is low (noise floods the useful signal), and noise signals are re-amplified due to the large energy contained therein, resulting in a worse signal-to-noise ratio. Therefore, in this embodiment, adding white gaussian noise before the PCA performs noise reduction processing on the first echo energy signal and the second echo energy signal to obtain a first noise-reduced echo energy signal and a second noise-reduced echo energy signal, properly improving the signal to noise ratio, and performing PCA noise reduction processing on the first noise-reduced echo energy signal and the second noise-reduced echo energy signal respectively by using the PCA to obtain a first noise-reduced echo signal and a second noise-reduced echo signal, specifically:
PCA is essentially a lossy feature compression process in which the most raw information is retained, and the dimensionality-reduced (projected) data points are as scattered as possible to achieve this goal. Referring to fig. 2, fig. 2 is a schematic diagram of PCA principal component analysis according to an embodiment of the present invention, compared to long arrows, if the PCA principal component analysis is performed on short arrows, more overlapping points will be provided, which means more information is lost, so that the embodiment selects the long arrow direction for projection. The degree of dispersion is expressed by variance, and the feature of the PCA after the dimension reduction is set to a, For variance, a i represents the value in feature A, m is the total number of samples, and since feature zero-mean processing will be done before PCA dimension reduction, it is noted that: similarly, in order to reduce redundant information of features, the features after PCA dimension reduction should be uncorrelated with each other, and the correlation is measured by covariance, and specifically, if two features after PCA dimension reduction are A, B Is 0.
Representing the first noise-reduced echo signal and the second noise-reduced echo signal asWherein, the subscript 1/2 of f 1/2 (t) indicates that the two signals are included, 1 is a respiratory signal, and 2 is a heartbeat signal. The present embodiment constructs a covariance matrix based on f 1/2 (t) and multiplies the covariance matrix by a coefficientDefining a covariance matrix R xx is expressed as:
As can be seen from equation (18), the diagonal element of equation (18) is the variance of each feature, and the elements at other locations are the covariances between the features. Next, the eigenvectors and eigenvalues of the covariance matrix are solved, and the conventional implementation is two, eigenvalue decomposition and SVD algorithm decomposition, and the eigenvalue decomposition method is used in this embodiment. Assuming that the eigenvalue of covariance matrix R xx is λ and the eigenvector is x, then:
Rxxx=λx (19)
Further derivations may be made:
(λE-Rxx)x=0 (20)
The eigenvalue λ and eigenvector x are obtained from equation (20), then:
Rxx＝xTΣx (21)
Wherein R xx represents the covariance matrix, x represents the eigenvectors of the covariance matrix, and Σ represents the matrix composed of eigenvalues. At this time, the eigenvalues are sorted from large to small in the matrix Σ, and the eigenvectors corresponding to the eigenvalues are also transformed accordingly. Each element in the finally obtained feature matrix sigma is the proportion of each IMF component after radar echo signal decomposition in the main component, a plurality of components with larger proportion are screened out as the main component by setting a fixed threshold value or adopting a self-adaptive threshold value determining method, a line of feature vectors corresponding to the feature value lambda and the first noise-carrying echo energy signal matrix are used for operation, namely the component of each first noise-carrying echo energy signal component after PCA noise reduction is obtained, and finally the screened components are added to obtain a first noise-carrying echo signal after PCA noise reduction, which is expressed as:
y1/2(t)＝xXT (22)
And similarly, a second noise-reduced echo signal can be obtained by processing the second noise-reduced echo energy signal.
S5, performing autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal respectively to extract a heartbeat signal and a respiratory signal.
Specifically, after PCA principal component analysis, heartbeat signals and respiratory signals are basically screened out. However, the noise is still quite large, and the conventional autocorrelation detection method uses the characteristics of uncorrelation between the signal and the noise and between the noise and the noise to achieve the purpose of improving the signal-to-noise ratio through autocorrelation operation on the input signal and the input signal delayed by τ, for example, the formula (22) is expressed as:
the autocorrelation function corresponding to equation (23) is expressed as:
RY(τ)＝E[y1/2(t)·y1/2(t+τ)]＝Rs(τ)+E[s(t)·n(t+τ)]+E[s(t+τ)·n(t)]+RN(τ) (24)
The autocorrelation function of the random process is replaced by the temporal autocorrelation function of the sample function, at which point the autocorrelation function in equation (24) is re-expressed as:
In the actual measurement, the actual observation time T is considered to be limited, and therefore, the formula (25) is re-expressed as:
the autocorrelation function of the signal of this embodiment is expressed as:
If the noise is standard Gaussian white noise, then E [ n (t) ], E [ n (t+τ) ] are both 0, so that E [ s (t) n (t+τ) ], E [ s (t+τ) n (t) ] are both 0. However, in actual measurement, since the observation time is limited and the degree of noise whitening is not necessarily very ideal, E [ n (t) ], E [ n (t+τ) ] are not necessarily zero. Thus, the correlation function of the signal and noise according to equation (26) is expressed as:
Although white gaussian noise is theoretically except τ=0, the remaining values are all 0. But in actual measurement the noise cannot reach what the theory envisages. Thus, R N (τ) (τ+.0) is always present and is a function of τ. However, the amplitude is inevitably greatly reduced compared with the original noise, and the noise can be regarded as new noise. As for R N (0), which is a relatively large number, it is not calculated at the time of actual measurement or simulation, but 0 is substituted, and equation (24) is re-expressed as:
changing the integration time from T- τ to T, equation (30) is simplified to:
Then this embodiment equation (24) is re-expressed as:
Wherein, Is the superposition of Rs (τ), E [ s (t+τ). N (t) ], and n' (t) is the superposition of E [ s (t). N (t+τ) ] and R N (τ).
The formulas (23) and (32) are compared, and the frequency is not changed although the amplitude and the phase of the signals are different. It increases the signal-to-noise ratio by correlation operations, but its degree of improvement is limited, thus limiting the ability to detect weak signals-this embodiment selects multiple autocorrelation, taking y' 1/2 (t) as y 1/2 (t), the above steps are repeated a plurality of times, and the more the number of times of autocorrelation, the more the signal-to-noise ratio is improved, so that a weak signal immersed in noise can be detected. While the normalized autocorrelation function value of random noise is maximum at zero point, the rest points are attenuated to zero immediately, while the normalized autocorrelation function value of general signal is maximum at zero point, the rest points are not attenuated to zero immediately, but go through slow down process. The randomness of the values of the random noise at each time point is shown as weak correlation, and the values of the general signals at each time point have certain correlation and are shown as strong correlation.
In this embodiment, the first noise-reduction echo signal and the second noise-reduction echo signal are respectively extracted through the multiple autocorrelation processing to obtain a heartbeat signal and a respiratory signal. The signal-to-noise ratio of the source signal can be improved by the single-correlation operation, but the capability of detecting and extracting weak signals is limited due to the limited improvement degree of the signal-to-noise ratio. The embodiment adopts multiple autocorrelation, and through the step of executing single correlation operation for multiple times, the signal to noise ratio is greatly improved, so that the noise reduction effect is obvious, and the analysis and the extraction of the heartbeat signal and the respiratory signal from the first noise reduction echo signal and the second noise reduction echo signal are more facilitated.
It should be noted that, the autocorrelation signals must be real signals, and the real part and the imaginary part of the complex signals are subjected to multiple autocorrelation processing respectively during operation, and after noise reduction, correlation is used for noise reduction by using the correlation between the real part and the imaginary part, and then addition is performed.
In order to verify the effectiveness of the weak human body feature identification method of the life detection radar provided by the embodiment, the following simulation experiment is used for further proving.
Firstly, breath and heartbeat signals are analyzed, modeling of radar echo signals is carried out on an MATLAB platform, a 2GHz radar is selected to emit single-frequency continuous sine waves, after the single-frequency continuous sine waves are modulated with the breath and heartbeat signals, gaussian noise is added, and finally the radar echo signals containing noise are obtained. Because the amplitude of the breathing signal is more than ten times of that of the heartbeat signal, the signal-to-noise ratio is set to be 10dB, and the signal-to-noise ratio of the heartbeat signal can reach between-10 dB and-15 dB. And then, starting to extract and process the heartbeat signal of the radar echo signal containing noise, namely firstly decomposing the radar echo signal into a plurality of IMF components with different frequency components by using CEEMDAN mode decomposition algorithm, calculating the IMF components corresponding to 0.05-0.8 Hz, the IMF components corresponding to 0.8-3.0 Hz and the energy of other IMF components, screening out the corresponding respiration and heartbeat signals by using an adaptive threshold, wherein the signal-to-noise ratio of the heartbeat signal is improved to a certain extent, but compared with the noise still weaker, further screening out the screened effective heartbeat signal again by using PCA principal component analysis to further adjust the signal-to-noise ratio, and finally obtaining the final heartbeat signal by multiple autocorrelation.
Referring to fig. 3 (a) -3 (b), fig. 4, and fig. 5, fig. 3 (a) -3 (b) are schematic diagrams of a radar echo signal with noise and a radar echo signal after denoising according to an embodiment of the present invention, fig. 4 is a schematic diagram of a time domain analysis result of the radar echo signal according to an embodiment of the present invention, and fig. 5 is a schematic diagram of an FFT image of the radar echo signal according to an embodiment of the present invention. Fig. 3 (a) is a radar echo signal with noise in the frequency domain, fig. 3 (b) is a radar echo signal with 0-4 hz low-pass digital filter, ω 1 = 0.23438 is a respiratory signal, almost submerged ω 2 = 0.99609 is a heartbeat signal, two signals are added with 10dB of noise, fig. 4 is a time domain analysis of the radar echo signal with low-pass digital filter in fig. 3 (b), fig. 5 is an FFT image of the radar echo signal with energy calculation analysis, it can be seen that there is almost only one prominent peak value in the low-frequency part (IMF 5-IMF 7) IMF component, and there are still several frequency points with larger energy in the higher frequency part, and for this case, the method of energy feature analysis is used to realize noise reduction in this embodiment.
Referring to fig. 6 (a) -6 (b), fig. 7 (a) -7 (b), fig. 6 (a) -6 (b) are schematic diagrams of simulation results of a respiratory signal and a heartbeat signal at a fixed respiratory rate and a heartbeat frequency provided by an embodiment of the present invention, wherein fig. 6 (a) is a schematic diagram of simulation results of a respiratory signal at a fixed respiratory rate and a heartbeat frequency provided by an embodiment of the present invention, fig. 6 (b) is a schematic diagram of simulation results of a heartbeat signal at a fixed respiratory rate and a heartbeat frequency provided by an embodiment of the present invention, and fig. 7 (a) -7 (b) is a schematic diagram of simulation results of a respiratory signal and a heartbeat signal at a fixed respiratory rate and a variable heartbeat frequency provided by an embodiment of the present invention, wherein fig. 7 (a) is a schematic diagram of simulation results of a respiratory signal at a fixed respiratory rate and a variable heartbeat frequency provided by an embodiment of the present invention. The fixed respiratory frequency is 0.23Hz and the heartbeat frequency is 1.50Hz in fig. 6 (a) -6 (b), the heartbeat signal frequency of the fixed respiratory frequency is 0.23Hz and the heartbeat signal frequency of the variable heartbeat frequency is increased from 1Hz to 2Hz in fig. 7 (a) -7 (b), the step is 0.01Hz, the weak human body characteristic identification method proposed by the embodiment can be seen from fig. 6 (a) -6 (b) and fig. 7 (a) -7 (b), the noise is basically eliminated, the measurement result is basically consistent with the set frequency, namely, the respectively extracted respiratory frequency and heartbeat frequency measurement result are basically consistent with the set frequency, and the heartbeat signal can be separated from the body movement signal by the method proposed by the embodiment.
In summary, the weak human body feature identification method of the life detection radar provided by the embodiment can separate the heartbeat signal from the body movement signal, and the heart rate value extracted from the signal and the heart rate value extracted from the electrocardiosignal have strong correlation.
Example two
On the basis of the first embodiment, please refer to fig. 8, fig. 8 is a schematic structural diagram of a weak human body feature recognition device for life detection radar according to an embodiment of the present invention, and the present embodiment provides a weak human body feature recognition device for life detection radar, which includes:
the data acquisition module 801 is configured to acquire a radar echo signal, where the radar echo signal includes a heartbeat signal and a respiration signal.
Specifically, the radar echo signal acquired in the data acquisition module 801 of the present embodiment is expressed as:
Wherein, β0＝2kr0,Lambda is the emission wavelength, d is the wall thickness, epsilon is the dielectric constant, omega 1＝2πf1,ω2＝2πf2,f1 and f 2 represent the frequency of respiration and heartbeat, respectively, delta 1 and delta 2 represent the amplitude of respiration and heartbeat, respectively, phi 2 is the constant phase.
The first data processing module 802 is configured to decompose the radar echo signal to obtain M IMF components.
Specifically, the decomposing the radar echo signal in the first data processing module 802 to obtain M IMF components in this embodiment includes:
and decomposing the radar echo signals by using CEEMDAN algorithm to obtain M IMF components.
The data calculation and analysis module 803 is configured to calculate energy of each IMF component, and analyze the energy to obtain a first echo energy signal and a second echo energy signal.
Specifically, in the data calculation and analysis module 803 of this embodiment, calculating the energy of each IMF component, and analyzing to obtain the first echo energy signal and the second echo energy signal includes:
calculating the energy of each IMF component to obtain an IMF energy vector;
Normalizing the IMF energy vector to obtain a normalized weight vector;
The first echo energy signal and the second echo energy signal are determined from the M IMF components according to the normalized weight vector.
The second data processing module 804 is configured to perform PCA noise reduction processing on the first echo energy signal and the second echo energy signal to obtain a first noise reduction echo signal and a second noise reduction echo signal, respectively.
Specifically, in the second data processing module 804 of this embodiment, performing PCA noise reduction processing on the first echo energy signal and the second echo energy signal to obtain a first noise reduction echo signal and a second noise reduction echo signal respectively further includes:
Adding noise to the first echo energy signal and the second echo energy signal to obtain a first noise-carrying echo energy signal and a second noise-carrying echo energy signal;
PCA noise reduction processing is carried out on the first noise-carrying echo energy signal and the second noise-carrying echo energy signal respectively to obtain a first noise-reducing echo signal and a second noise-reducing echo signal.
The data extraction module 805 is configured to perform autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal to extract a heartbeat signal and a respiratory signal respectively.
Specifically, in the data extraction module 805 of this embodiment, performing autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal to extract a heartbeat signal and a respiratory signal respectively includes:
and performing multiple autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal respectively to extract a heartbeat signal and a respiratory signal.
Further, the frequency range of the heartbeat signal is 0.8 Hz-3.0 Hz.
Further, the frequency range of the breathing signal is 0.05 Hz-0.8 Hz.
The weak human body feature identification device of the life detection radar provided by the embodiment can execute the weak human body feature identification method embodiment of the life detection radar described in the above embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
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