CN113576461A - Method for identifying weak human body features of life detection radar - Google Patents

Method for identifying weak human body features of life detection radar Download PDF

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CN113576461A
CN113576461A CN202110681082.0A CN202110681082A CN113576461A CN 113576461 A CN113576461 A CN 113576461A CN 202110681082 A CN202110681082 A CN 202110681082A CN 113576461 A CN113576461 A CN 113576461A
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李震
杨昀
包敏
李睿堃
何其泽
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Shanghai Fire Research Institute of MEM
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Abstract

The invention discloses a method for identifying weak human body characteristics of a life detection radar, which comprises the steps of obtaining radar echo signals, wherein the radar echo signals comprise heartbeat signals and breathing signals; decomposing the radar echo signal 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; performing PCA noise reduction processing 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 self-correlation processing on the first noise-reduction echo signal and the second noise-reduction echo signal to extract a heartbeat signal and a respiration signal. The invention can separate the heartbeat signal from the body motion signal, and the heart rate value extracted from the signal has strong correlation with the heart rate value extracted from the electrocardio signal.

Description

Method for identifying weak human body features of life detection radar
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a method for identifying weak human body characteristics of a life detection radar.
Background
The biological radar uses electromagnetic wave as medium, transmits electromagnetic pulse, receives echo signal after being reflected by human body, processes and analyzes the echo signal, and obtains physiological information of the life body. The biological radar overcomes the problems that laser and infrared detection are seriously influenced by temperature and are blocked and invalid when meeting objects, and also overcomes the problems that the ultrasonic detection space has large propagation attenuation, is interfered by the reflection of environmental sundries and is blocked and invalid by water, ice and soil. 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 scientific and technological community.
Compared with the traditional contact monitoring, the non-contact monitoring method can reduce the discomfort of the electrode, the lead and the sensor to the target to be monitored, can overcome the monitoring to the patient who is difficult to use the traditional contact monitoring method, and can also avoid the influence of the problem of the monitoring caused by the possible falling of the contact device. Just because the biological radar has above-mentioned characteristics, biological radar can be used to realize the interval certain distance, pierces through the medium of certain thickness and carries out non-contact detection to human vital sign. The vital sign signals are composed of a plurality of parameters such as respiration, heartbeat, blood pressure, blood oxygen concentration and the like, and the signals are usually weak and are easily submerged in a strong clutter background. Breathe and heartbeat signal, breathe and can arouse the thorax burden motion to produce positive and negative Doppler frequency domain, be received by the receiver after modulating with the high frequency signal of radar transmission, the subsequent filtering operation that carries on of receiver can extract the respiratory signal, the heartbeat signal is the same reason, because the multiple spot reflection of thorax and the inhomogeneous distribution that can produce many frequencies again are near the harmonic of zero-frequency. Data acquisition and analysis indicated that: the biological radar signal processing can simplify the signal extraction of the harmonic model interfered by the Gaussian colored noise, clutter suppression and the processing for improving the signal to noise ratio. The biological radar can detect the vital signals (breathing, body movement and the like) of a human body in a non-contact manner, and is widely applied to occasions such as families, community hospitals and the like, and the existing biological radar adopts radar detection through blood pressure and radar detection through breathing signals. Wherein, the detection of the blood pressure by adopting the radar comprises the following steps: collecting an aortic pulse waveform signal of a subject by using a radar; extracting characteristic points of the aortic pulse waveform signals to obtain each main characteristic point; determining the mapping relation between the time period of the aortic pulse and each characteristic point; solving the pulse wave conduction time from two characteristic points of the first contraction peak of the aortic pulse and the stopping point of the ejection period, namely the dicrotic wave valley according to the mapping relation; solving a corresponding blood pressure value according to the conduction time, and further realizing the detection of the human body vital signal according to the detected blood pressure value; the respiratory signal is easy to detect by adopting a radar, the respiratory signal can be extracted by sampling a simple digital filter with the cut-off frequency of 0.8Hz, and then the detection of the human body vital signal is realized according to the detected respiratory signal.
However, in practical applications, it is necessary to detect a heartbeat signal of a human body so as to better reflect the life condition of the human body, but a band-pass filter (with a lower cut-off frequency of 0.8Hz and an upper cut-off frequency of 3Hz) is directly used to detect the heartbeat signal, and higher harmonics of a respiratory signal and environmental gaussian noise are also detected, so that it is difficult to extract the heartbeat signal.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for identifying weak human body features of a life detection radar.
One embodiment of the invention provides a method for identifying weak human body features of a life detection radar, which comprises the following steps:
acquiring a radar echo signal, wherein the radar echo signal comprises a heartbeat signal and a respiration signal;
decomposing the radar echo signal 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;
performing PCA noise reduction processing 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 self-correlation processing on the first noise-reduction echo signal and the second noise-reduction echo signal to extract a heartbeat signal and a respiration signal.
In one embodiment of the invention, the acquired radar return signals are represented as:
Figure BDA0003122527400000031
wherein the content of the first and second substances,
Figure BDA0003122527400000032
β0＝2kr0，
Figure BDA0003122527400000033
λ is the emission wavelength, d is the wall thickness, ε is the dielectric constant, ω1＝2πf1，ω2＝2πf2，f1And f2Representing the frequency of breathing and heartbeat, respectively, Delta1And Δ2Respectively representing the amplitude of respiration and heartbeat, phi2Is a constant phase.
In an embodiment of the present invention, decomposing the radar echo signal to obtain M IMF components includes:
and decomposing the radar echo signal by using a CEEMDAN algorithm to obtain the M IMF components.
In an embodiment of the present invention, calculating the energy of each IMF component separately, and analyzing 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;
and determining the first echo energy signal and the second echo energy signal from the M IMF components according to the normalized weight vector.
In an embodiment of the present invention, the performing PCA noise reduction on the first echo energy signal and the second echo energy signal to obtain a first noise-reduced echo signal and a second noise-reduced echo signal respectively further includes:
respectively adding noise to the first echo energy signal and the second echo energy signal to obtain a first noisy echo energy signal and a second noisy echo energy signal;
and performing PCA noise reduction processing on the first noisy echo energy signal and the second noisy echo energy signal respectively to obtain a first noise reduction echo signal and a second noise reduction echo signal.
In an embodiment of the present invention, the performing autocorrelation processing on the first noise-reduced echo signal and the second noise-reduced 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 respiration signal.
In one embodiment of the present invention, the frequency range of the heartbeat signal is 0.8Hz to 3.0 Hz.
In one embodiment of the invention, the respiratory signal frequency range is 0.05Hz to 0.8 Hz.
Compared with the prior art, the invention has the beneficial effects that:
the method for identifying the weak human body features of the life detection radar can separate heartbeat signals from body motion signals, and heart rate values extracted from the signals have strong correlation with heart rate values extracted from electrocardiosignals.
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 features 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) to fig. 3(b) are schematic diagrams of a radar echo signal containing noise and a denoised radar echo signal according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a time domain analysis result of a radar echo signal according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an FFT image of a radar echo signal under different IMF components according to an embodiment of the present invention;
FIGS. 6(a) -6 (b) are schematic diagrams illustrating simulation results of a respiration signal and a heartbeat signal at a fixed respiration frequency and a fixed heartbeat frequency according to an embodiment of the present invention;
FIGS. 7(a) -7 (b) are schematic diagrams illustrating simulation results of respiration signals and heartbeat signals at a fixed respiration rate and a variable heartbeat rate according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a device for identifying weak human body features 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 the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart 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 method for identifying weak human body features of a life detection radar, which comprises the following steps:
and S1, acquiring radar echo signals, wherein the radar echo signals comprise heartbeat signals and respiration signals.
Specifically, in this embodiment, the radar transmits a single-frequency continuous sine wave, and the complex signal thereof is represented as:
Figure BDA0003122527400000051
wherein, U0Representing complex signal amplitude, omega0Representing 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)
wherein r is0Represents the distance between the living target and the radar (including the thickness of the wall), and Δ r (t) represents the distance change caused by the breathing and heartbeat of the target. The echo signal received by the radar is then expressed as:
Figure BDA0003122527400000061
where μ denotes the attenuation factor (including the two-way attenuation in free space and during penetration through walls), a denotes the amplitude modulation of the echo by the target, β0And 2k Δ r (t) represents a phase shift, β, respectively0＝2kr0，
Figure BDA0003122527400000062
λ 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, additional time delay is generated when the electromagnetic waves penetrate through the wall, and 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 represented again as:
Figure BDA0003122527400000063
wherein the content of the first and second substances,
Figure BDA0003122527400000064
breathing and heartbeat are generally seen as simple vibrations, which cause fluctuations in the distance of the radar from a living target represented as:
Δr(t)＝Δ1sin(ω1t)+Δ2sin(ω2t+φ2) (5)
wherein, ω is1＝2πf1，ω2＝2πf2，f1And f2Representing the frequency of breathing and heartbeat, respectively, Delta1And Δ2Respectively representing the amplitude of respiration and heartbeat, phi2Is a constant phase. The doppler signal is embodied in the phase of the echo, and the present embodiment removes the carrier frequency by using a quadrature phase detection method, and obtains a zero intermediate frequency signal, which is expressed as:
Figure BDA0003122527400000065
the normalization processing is performed on the formula (4) through the formula (6), and the normalized signal is expressed as:
Ss(t)＝exp{-jφ0-j2k[Δ1 sin(ω1t)+Δ2sin(ω2t+φ2)]} (7)
since the phase contains the sine expression, in order to analyze the relationship between the sine fundamental wave and each harmonic, the present embodiment uses the bessel function to express as:
Figure BDA0003122527400000071
then equation (7) is expanded using equation (8) as:
Figure BDA0003122527400000072
wherein m1 and m2 are integers. As can be seen from equation (9), the frequency spectrum of the radar echo signal of the present embodiment includes 3 parts:
(1) constant ingredient when m 1-m 2-0;
(2) fundamental angular frequency omega of respiration and heartbeat1And ω2；
(3) Respiration and heartbeat angleCombination m of frequency harmonics1ω1+m2ω2。
And S2, decomposing the radar echo signal to obtain M IMF components.
Specifically, the decomposing of the radar echo signal to obtain M connotation Mode components (IMF for short) includes: and decomposing the radar echo signal by using a Complete integration empirical mode decomposition algorithm (Complete EEMD with Adaptive Noise, CEEMDAN for short) of self-Adaptive Noise to obtain M IMF components. Specifically, the method comprises the following steps:
since both the respiration signal and the heartbeat signal are present in the echo signal, we should first separate the respiration and heartbeat signals because the small signal (heartbeat signal) is easily filtered out as clutter of the large signal (respiration signal) when the signal is de-noised. According to the difference between the respiratory signal and the heartbeat signal, the radar echo signal obtained from S1 is decomposed into a plurality of IMF components according to the signal peak value and the corresponding frequency point by using the CEEMDAN algorithm, so that two effective signals, namely the respiratory signal and the heartbeat signal, can be conveniently separated. Specifically, in this embodiment, a finite number of times of adaptive white noise is added, and the modal IMF component obtained by decomposition is used in the CEEMDAN algorithm
Figure BDA0003122527400000081
And (4) showing. Operator Ej(. -) represents the sequence of the j-th modal IMF component of a given signal obtained by the CEEMDAN algorithm, ωiWhite Gaussian noise, S, according to N (0,1) added for the ith times(t) is a radar echo signal sequence obtained in S1, and is represented as S after white noise is added theretos(t)＝ε0ωi(t), decomposing the radar echo signal sequence added with the white noise by using a CEEMDAN algorithm, wherein the process is as follows:
firstly, decomposing by using CEEMDAN algorithm to obtain the 1 st modal component
Figure BDA0003122527400000082
Then, 1 st residual margin signal is calculated
Figure BDA0003122527400000083
Using CEEMDAN algorithm to signal R1(t)+ε1E1ωi(t) repeating the decomposition for N times to obtain the 2 nd modal component
Figure BDA0003122527400000084
For k 2,3, …, M, the k-th residual margin is calculated
Figure BDA0003122527400000085
And (3) repeating the radar echo signal sequence decomposed by using the CEEMDAN algorithm, and calculating the (k + 1) th modal component as follows:
Figure BDA0003122527400000086
and (5) performing multiple operations until the residual margin is not suitable for being decomposed, and finishing the decomposition. The final residual margin is expressed as:
Figure BDA0003122527400000087
finally, the radar echo signal of the present embodiment is decomposed into M modal components IMF, which are expressed as:
Figure BDA0003122527400000088
and 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 of this embodiment is processed by the CEEMDAN algorithm to obtain M IMF components, and each IMF component spectrum has a corresponding center frequency, which represents the target frequency or the noise frequency. Since the target information is unknown, it is difficult to extract the target signal features from the perspective of signal spectrum analysis. Each IMF component energy spectrum of the signal can better show the energy variation of different target IMF components, so this embodiment proposes a feature extraction method based on the IMF component energy ratio, and specifically, S3 includes S3.1, S3.2, S3.3:
and S3.1, calculating the energy of each IMF component to obtain an IMF energy vector.
Specifically, the present embodiment calculates the energy E of each IMF componentiExpressed as:
Figure BDA0003122527400000091
thereby obtaining IMF energy vectors V corresponding to M IMF componentsE＝[E1,E2,…,EM]。
And S3.2, carrying out normalization processing on the IMF energy vector to obtain a normalized weight vector.
Specifically, the present embodiment integrates the IMF energy vector VEAnd carrying out normalization processing to obtain a normalized weight vector which is expressed as:
V′E＝[p1,p2,…,pM] (14)
wherein the content of the first and second substances,
Figure BDA0003122527400000092
the normalized weight of each IMF component is represented,
Figure BDA0003122527400000093
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 favorable for highlighting the principal component of the signal and reducing the bandwidth influence on the IMF energy analysis. The energy difference characteristics of high and low frequency bands of the signal analyze the difference of different sound target vibration mechanisms, so that the energy distribution characteristics of each IMF component of target radiation noise are obviously different, but the target radiation noise exists due to the existence of environmental noise or measurement system noiseThe noise measurement signal is influenced by the IMF energy vector VEThe target feature parameters do not enable target recognition. Therefore, according to the characteristics of the target, defining the high-low frequency energy difference of the target signal, using the high-low frequency energy difference as the signal characteristic parameter, and passing through the frequency band energy difference characteristic of the target signal and the IMF energy vector VEAnd analysis is combined to realize target identification and classification.
Supposing that a radar echo signal is decomposed to obtain M IMF components, the ith IMF component has L sampling points, wherein the instantaneous frequency of the p sampling point is fi,pInstantaneous amplitude of Ai,pThen p point instantaneous intensity is
Figure BDA0003122527400000101
According to the frequency distribution characteristics of typical radar echo signals, frequency values are defined to be a low frequency band at 50-800 Hz, and a high frequency band at 800-3000 Hz. The instantaneous intensity of the time-frequency sampling point in the low frequency band is recorded as Bl1,Bl2,…,BlnN is the number of time-frequency sampling points in the low frequency band, the total energy P of the radar echo signal in low frequencylExpressed as:
Figure BDA0003122527400000102
similarly, the instantaneous intensity of the time-frequency sampling point in the high frequency band is recorded as Bl1,Bl2,…,BlmM is the number of time-frequency sampling points in the high frequency band, the total energy P of the radar echo signal in high frequencyhExpressed as:
Figure BDA0003122527400000103
then, the energy difference between high and low frequencies of the radar echo signal is defined as:
ΔP＝Ph-Pl (17)
after the energy characteristic analysis and noise reduction, two sections of signals can be obtained, namely a first echo energy signal f of 0.05 Hz-0.8 Hz1(t) signal sumSecond echo energy signal f from 0.8Hz to 3Hz2(t) the main components of the signals at the two ends respectively correspond to the respiration signal and the heartbeat signal.
And S4, performing PCA noise reduction processing 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 principal component analysis of PCA screens principal components in the signal, using PCA when the signal-to-noise ratio is low (noise overwhelms the useful signal) is likely to filter the useful signal as noise, and the noise signal is amplified again because it contains more energy, making the signal-to-noise ratio worse. Therefore, in this embodiment, before the PCA performs noise reduction on the first echo energy signal and the second echo energy signal, gaussian white noise is added to obtain a first noisy echo energy signal and a second noisy echo energy signal, the signal-to-noise ratio is properly increased, and then the PCA is used to perform PCA noise reduction on the first noisy echo energy signal and the second noisy echo energy signal respectively to obtain a first noise reduction echo signal and a second noise reduction echo signal, specifically:
PCA is essentially a lossy feature compression process that retains the most raw information, and to achieve this, the data points after dimensionality reduction (projection) are as scattered as possible. Referring to fig. 2, fig. 2 is a schematic diagram of principal component analysis of PCA provided in the embodiment of the present invention, compared to a long arrow, if a short arrow is projected, there are more overlapped points, which means more information is lost, and thus the long arrow is selected for projection in the present embodiment. The degree of dispersion is expressed by the variance: let the feature after the dimensionality reduction of the PCA be A,
Figure BDA0003122527400000111
is the variance, aiRepresenting the value in the characteristic A, wherein m is the total number of samples, and before the PCA dimensionality reduction, the characteristic zero-averaging processing is carried out and is recorded as:
Figure BDA0003122527400000112
similarly, in order to reduce redundant information of features, features after PCA dimensionality reduction should not mutually differCorrelation is measured by covariance, specifically, two features after PCA dimensionality reduction are A, B, then
Figure BDA0003122527400000113
Is 0.
Representing the first and second noise-reduced echo signals as
Figure BDA0003122527400000121
Wherein f is1/2The (t) subscript 1/2 indicates that two signals are included, 1 being the respiration signal and 2 being the heartbeat signal. This embodiment is based on1/2(t) constructing a covariance matrix and multiplying the covariance matrix by the coefficients
Figure BDA0003122527400000122
Defining a covariance matrix RxxExpressed as:
Figure BDA0003122527400000123
as can be seen from equation (18), the diagonal elements of equation (18) are the variances of the features, and the elements of other positions are the covariances between the features. Then, solving eigenvectors and eigenvalues of the covariance matrix, and the traditional implementation methods have two types: eigenvalue decomposition and SVD algorithm decomposition, the present embodiment uses an eigenvalue decomposition method. Hypothesis covariance matrix RxxIs λ, and the eigenvector is x, then:
Rxxx＝λx (19)
further derivation can yield:
(λE-Rxx) x is 0 (20), and the eigenvalue λ and eigenvector x are obtained from equation (20), then:
Rxx＝xTΣx (21)
wherein R isxxRepresents a covariance matrix, x represents an eigenvector of the covariance matrix, and Σ represents a matrix formed by eigenvalues. In this case, the eigenvalues are sorted from large to small in the matrix Σ, and the eigenvectors corresponding to the eigenvalues are also converted accordingly. To obtain finallyEach element in the feature matrix Σ is a proportion of each IMF component in a principal component after decomposition of the radar echo signal, a few components with a larger proportion are screened out as principal components by setting a fixed threshold or adopting a method of adaptively determining the threshold, a component obtained by performing PCA noise reduction on each first noisy echo energy signal component is obtained by using a line of feature vectors corresponding to a feature value λ and a first noisy echo energy signal matrix, and finally the screened components are added to obtain a first noise reduction echo signal after PCA noise reduction, which is expressed as:
y1/2(t)＝xXT (22)
similarly, a second noisy echo energy signal can be obtained, and a second noise-reduced echo signal is obtained through the processing.
And 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 respiration signal.
Specifically, after PCA principal component analysis, the heartbeat signal and the respiration signal are basically screened out. However, the noise is still large, and the conventional autocorrelation detection method is to perform autocorrelation operation on the input signal and the input signal delayed by τ, and use the characteristics of the uncorrelated signal and noise to achieve the purpose of improving the signal-to-noise ratio, for example, equation (22) is expressed as:
Figure BDA0003122527400000131
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 time autocorrelation function of the sample function is used instead of the autocorrelation function of the random process, and at this time, the autocorrelation function in equation (24) is again expressed as:
Figure BDA0003122527400000132
in actual measurement, considering that the actual observation time T is finite, equation (25) is re-expressed as:
Figure BDA0003122527400000141
the autocorrelation function of the signal of this embodiment is expressed as:
Figure BDA0003122527400000142
if the noise is standard white Gaussian noise, then E [ n (t) ] and E [ n (t + τ) ] are both 0, and E [ s (t) + τ) ] and E [ s (t + τ) n (t)) ] are both 0. However, in actual measurement, the observation time is limited, and the degree of noise whitening is not necessarily perfect, so that E [ n (t) ], E [ n (t + τ) ] are not necessarily zero. Thus, the signal to noise correlation function is expressed according to equation (26) as:
Figure BDA0003122527400000143
Figure BDA0003122527400000144
although white gaussian noise theoretically has 0 as the value except τ. But in actual measurement, the noise cannot reach what is theoretically assumed. Thus, RN(τ) (τ ≠ 0) is always present and is a function of τ. But the amplitude of the noise is necessarily greatly reduced compared with the original noise, and the noise can be regarded as new noise. As for RN(0) Is a relatively large number, which may not be calculated during measurement or simulation, but is replaced by 0, equation (24) is re-expressed as:
Figure BDA0003122527400000145
changing the integration time from T- τ to T, equation (30) is reduced to:
Figure BDA0003122527400000146
then the formula (24) in this embodiment is re-expressed as:
Figure BDA0003122527400000147
wherein the content of the first and second substances,
Figure BDA0003122527400000151
is Rs (τ), E [ s (t + τ) · n (t)]Superposition of (2); n' (t) is E [ s (t) & n (t + T)]And RN(τ) superposition.
Comparing equations (23) and (32), the frequency is not changed although the amplitude and phase of the signals are different. It increases the signal-to-noise ratio through correlation operation, but the improvement degree is limited, thereby limiting the capability of detecting weak signals.1/2(t) as y1/2(t), repeating the above steps for a plurality of times, the more the number of times of autocorrelation, the more the signal-to-noise ratio is improved, and thus weak signals buried in noise can be detected. The normalized autocorrelation function value of the random noise is maximum at the zero point, and the other points are immediately attenuated to zero; while the value of the normalized autocorrelation function of the general signal is maximum at the zero point, the rest points are not immediately attenuated to zero, but are subjected to a slow descending process. The randomness of the values of all time points of the random noise is expressed as weak correlation, while the values of all time points of the general signal have certain correlation and are expressed as strong correlation.
In this embodiment, the first noise reduction echo signal and the second noise reduction echo signal are respectively subjected to the multiple autocorrelation processing to extract a heartbeat signal and a respiration signal. Although the single correlation operation can improve the signal-to-noise ratio of the source signal, the ability to detect and extract weak signals is limited due to the limited degree of improvement of the signal-to-noise ratio. The embodiment adopts multiple autocorrelation, greatly improves the signal-to-noise ratio by executing the step of single correlation operation for many times, has obvious noise reduction effect, and is more favorable for analyzing and extracting heartbeat signals and breathing signals from the first noise reduction echo signals and the second noise reduction echo signals respectively.
It should be noted that the autocorrelation signal must be a real signal, and in the operation, the real part and the imaginary part of the complex signal are respectively subjected to multiple autocorrelation processing, and after noise reduction, the correlation between the real part and the imaginary part is utilized to reduce noise by using cross-correlation, and then the noise is added.
In order to verify the effectiveness of the method for identifying the weak human body features of the life detection radar provided by the embodiment, the following simulation experiment is used for further proving.
Firstly, respiration and heartbeat signals are analyzed, radar echo signals are modeled on an MATLAB platform, a 2GHz radar is selected to emit single-frequency continuous sine waves, the single-frequency continuous sine waves are modulated with the respiration and heartbeat signals, and Gaussian noise is added to obtain the final radar echo signals containing noise. Because the amplitude of the respiration signal is more than ten times 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. Then, the heartbeat signal extraction processing is started to be carried out on the radar echo signal containing the noise: firstly, decomposing a radar echo signal into a plurality of IMF components with different frequency components by using a CEEMDAN modal decomposition algorithm, calculating the energy of the IMF component corresponding to 0.05 Hz-0.8 Hz, the IMF component corresponding to 0.8 Hz-3.0 Hz and other IMF components, screening out corresponding respiration and heartbeat signals through a self-adaptive threshold value, wherein the signal-to-noise ratio of the heartbeat signals is improved to a certain extent, but is still weaker than noise, further screening the screened effective heartbeat signals again by using PCA principal component analysis to further increase the signal-to-noise ratio, and finally obtaining the final heartbeat signal through multiple autocorrelation.
Referring to fig. 3(a) -3 (b), 4 and 5, fig. 3(a) -3 (b) are schematic diagrams of a radar echo signal containing noise and a denoised radar echo signal 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 the embodiment of the present invention, and fig. 5 is a schematic diagram of a time domain analysis result of the radar echo signal according to the embodiment of the present inventionSchematic representation of the FFT image. FIG. 3(a) shows a frequency-domain noisy radar echo signal, FIG. 3(b) shows a radar echo signal passing through a 0-4 Hz low-pass digital filter, ω10.23438 is the breathing signal, nearly flooded ω20.99609, where the two signals have 10dB of noise added to them; FIG. 4 is a time domain analysis of the radar echo signal of FIG. 3(b) after passing through a low-pass digital filter; fig. 5 is an FFT image of a radar echo signal after energy calculation and analysis, and it can be seen that the IMF component has almost only one outstanding peak value in the low frequency part (IMF 5-IMF 7), and there are still several frequency points with larger energy in the part with higher frequency, for which case the embodiment uses an energy feature analysis method to implement noise reduction.
Referring to fig. 6(a) -6 (b) and 7(a) -7 (b), fig. 6(a) -6 (b) are schematic diagrams of simulation results of a respiration signal and a heartbeat signal at a fixed respiration frequency and a fixed heartbeat frequency according to an embodiment of the present invention, where fig. 6(a) is a schematic diagram of simulation results of a respiration signal at a fixed respiration frequency and a fixed heartbeat frequency according to an embodiment of the present invention, fig. 6(b) is a schematic diagram of simulation results of a heartbeat signal at a fixed respiration 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 a respiration signal and a heartbeat signal at a fixed respiration frequency and a variable heartbeat frequency according to an embodiment of the present invention, where fig. 7(a) is a schematic diagram of simulation results of a respiration signal at a fixed respiration frequency and a variable heartbeat frequency according to an embodiment of the present invention, fig. 7(b) is a schematic diagram of a simulation result of a heartbeat signal under a fixed breathing frequency and a variable heartbeat frequency according to an embodiment of the present invention. In fig. 6(a) to 6(b), the fixed respiratory frequency is 0.23Hz and the heartbeat frequency is 1.50Hz, in fig. 7(a) to 7(b), the heartbeat signal frequency of the fixed respiratory frequency is 0.23Hz and the variable heartbeat frequency signal is increased from 1Hz to 2Hz, and the step length is 0.01Hz, and it can be seen from fig. 6(a) to 6(b) and 7(a) to 7(b) that after the weak human body feature identification method provided by the present embodiment, the noise is substantially eliminated, and the measurement result substantially matches the set frequency, that is, the respiratory frequency and the heartbeat frequency measurement result respectively extracted substantially match the set frequency, and the method provided by the present embodiment can separate the heartbeat signal from the body motion signal.
In summary, the method for identifying the weak human body features of the life detection radar provided by the embodiment can separate the heartbeat signal from the body motion signal, and the heart rate value extracted from the signal has strong correlation with the heart rate value extracted from the electrocardiosignal.
Example two
On the basis of the first embodiment, please refer to fig. 8, where fig. 8 is a schematic structural diagram of a weak human body feature identification device of a life detection radar according to an embodiment of the present invention, in which the embodiment provides a weak human body feature identification device of a life detection radar, and the weak human body feature identification device of a life detection radar includes:
and a data acquisition module 801, 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 by the data acquisition module 801 of this embodiment is represented as:
Figure BDA0003122527400000181
wherein the content of the first and second substances,
Figure BDA0003122527400000182
β0＝2kr0，
Figure BDA0003122527400000183
λ is the emission wavelength, d is the wall thickness, ε is the dielectric constant, ω1＝2πf1，ω2＝2πf2，f1And f2Representing the frequency of breathing and heartbeat, respectively, Delta1And Δ2Respectively representing the amplitude of respiration and heartbeat, phi2Is a 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 signal by using a CEEMDAN algorithm to obtain M IMF components.
And 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, the calculating and analyzing module 803 of the data of this embodiment respectively calculates the energy of each IMF component, and the 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;
and determining a first echo energy signal and a second echo energy signal from the M IMF components according to the normalized weight vector.
The second data processing module 804 is configured to perform PCA noise reduction 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, in this embodiment, the obtaining the first noise-reduced echo signal and the second noise-reduced echo signal by performing PCA noise reduction on the first echo energy signal and the second echo energy signal in the second data processing module 804 further includes:
respectively adding noise to the first echo energy signal and the second echo energy signal to obtain a first noisy echo energy signal and a second noisy echo energy signal;
and performing PCA noise reduction processing on the first noisy echo energy signal and the second noisy echo energy signal respectively to obtain a first noise reduction echo signal and a second noise reduction echo signal.
The data extraction module 805 is configured to perform autocorrelation processing on the first noise-reduced echo signal and the second noise-reduced echo signal respectively to extract a heartbeat signal and a respiration signal.
Specifically, the obtaining of the heartbeat signal and the respiration signal by performing autocorrelation processing on the first noise-reduced echo signal and the second noise-reduced echo signal in the data extraction module 805 of this embodiment 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 respiration signal.
Furthermore, the frequency range of the heartbeat signal is 0.8 Hz-3.0 Hz.
Furthermore, the frequency range of the respiratory signal is 0.05 Hz-0.8 Hz.
The weak human body feature identification device of the life detection radar provided by this embodiment may implement the embodiment of the weak human body feature identification method of the life detection radar described in the first embodiment, and the implementation principle and the technical effect are similar, which are not described herein again.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (8)

1. A method for identifying weak human body features of a life detection radar is characterized by comprising the following steps:
acquiring a radar echo signal, wherein the radar echo signal comprises a heartbeat signal and a respiration signal;
decomposing the radar echo signal 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;
performing PCA noise reduction processing 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 self-correlation processing on the first noise-reduction echo signal and the second noise-reduction echo signal to extract a heartbeat signal and a respiration signal.
2. The method for identifying the weak human body features of the life detection radar as claimed in claim 1, wherein the obtained radar echo signals are expressed as:
Figure FDA0003122527390000011
wherein the content of the first and second substances,
Figure FDA0003122527390000012
β0＝2kr0，
Figure FDA0003122527390000013
λ is the emission wavelength, d is the wall thickness, ε is the dielectric constant, ω1＝2πf1，ω2＝2πf2，f1And f2Representing the frequency of breathing and heartbeat, respectively, Delta1And Δ2Respectively representing the amplitude of respiration and heartbeat, phi2Is a constant phase.
3. The method for identifying the weak human body features of the life detection radar as claimed in claim 1, wherein decomposing the radar echo signal to obtain M IMF components comprises:
and decomposing the radar echo signal by using a CEEMDAN algorithm to obtain the M IMF components.
4. The method for identifying the weak human body features of the life detection radar as claimed in claim 1, wherein the step of calculating the energy of each IMF component and analyzing the energy of the first echo energy signal and the second echo energy signal comprises:
calculating the energy of each IMF component to obtain an IMF energy vector;
normalizing the IMF energy vector to obtain a normalized weight vector;
and determining the first echo energy signal and the second echo energy signal from the M IMF components according to the normalized weight vector.
5. The method for identifying the weak human body features of the life detection radar as claimed in claim 1, wherein the step of performing PCA noise reduction on the first echo energy signal and the second echo energy signal to obtain a first noise-reduced echo signal and a second noise-reduced echo signal further comprises:
respectively adding noise to the first echo energy signal and the second echo energy signal to obtain a first noisy echo energy signal and a second noisy echo energy signal;
and performing PCA noise reduction processing on the first noisy echo energy signal and the second noisy echo energy signal respectively to obtain a first noise reduction echo signal and a second noise reduction echo signal.
6. The method for identifying the weak human body features of the life detection radar as claimed in claim 1, wherein the obtaining of the heartbeat signal and the respiration signal by performing the autocorrelation processing on the first noise reduction echo signal and the second noise reduction echo signal respectively comprises:
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 respiration signal.
7. The method for identifying the weak human body features of the life detection radar as claimed in claim 1, wherein the frequency range of the heartbeat signal is 0.8 Hz-3.0 Hz.
8. The method for identifying the weak human body characteristics of the life detection radar as claimed in claim 1, wherein the frequency range of the respiration signal is 0.05 Hz-0.8 Hz.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100152600A1 (en) * 2008-04-03 2010-06-17 Kai Sensors, Inc. Non-contact physiologic motion sensors and methods for use
CN103529436A (en) * 2013-10-12 2014-01-22 南京信息工程大学 Method for carrying out separation and time-frequency analysis on respiration and heartbeat signals in non-contact life detection on basis of HHT (Hilbert Huang Transform)
CN110507293A (en) * 2019-07-26 2019-11-29 中国电子科技集团公司第三十八研究所 A kind of ultra-broadband wall-through radar human body respiration and heartbeat detecting method and system
CN112014692A (en) * 2020-07-20 2020-12-01 国网安徽省电力有限公司电力科学研究院 Partial discharge ultrahigh frequency signal blind source separation denoising method based on principal component analysis

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100152600A1 (en) * 2008-04-03 2010-06-17 Kai Sensors, Inc. Non-contact physiologic motion sensors and methods for use
CN103529436A (en) * 2013-10-12 2014-01-22 南京信息工程大学 Method for carrying out separation and time-frequency analysis on respiration and heartbeat signals in non-contact life detection on basis of HHT (Hilbert Huang Transform)
CN110507293A (en) * 2019-07-26 2019-11-29 中国电子科技集团公司第三十八研究所 A kind of ultra-broadband wall-through radar human body respiration and heartbeat detecting method and system
CN112014692A (en) * 2020-07-20 2020-12-01 国网安徽省电力有限公司电力科学研究院 Partial discharge ultrahigh frequency signal blind source separation denoising method based on principal component analysis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
钱荣君: "基于雷达的人体呼吸心跳信号检测和行为识别技术", 《中国优秀硕士学位论文全文数据库信息科技辑》, pages 16 - 17 *

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