CN113702968A - Life detection method and device - Google Patents
Life detection method and device Download PDFInfo
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Abstract
The application provides a life detection method and a device thereof, wherein the method comprises the following steps: detecting by adopting an ultra-wideband radar to obtain a radar echo; receiving pulses for radar in a radar echo along a slow time direction, and determining a permutation entropy PE value; determining the position of the human body object according to the PE value; vital sign signals are extracted from the radar echoes according to the position of the human subject. Therefore, the position of the human body object can be determined according to the PE value of the radar receiving pulse in the radar echo along the slow time direction, and the vital sign signal can be effectively extracted from the radar echo according to the position of the human body object.
Description
Technical Field
The present application relates to the field of signal processing technologies, and in particular, to a method and an apparatus for life detection.
Background
An Ultra-Wideband Radar (UWB for short) has the advantages of high distance resolution, strong penetration capability, low power consumption, strong anti-interference capability and the like, so that the position, the respiration, the heartbeat and other information of a human body object can be detected in a non-contact manner and in a long distance. Because weak sign signals generated by the micromotion of the human body object are submerged in strong noise, how to extract vital sign signals such as respiration and heartbeat is of great importance.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
The application provides a life detection method and a life detection device, which are used for determining the position of a human body object according to a PE value of a radar receiving pulse in a radar echo along a slow time direction, and effectively extracting a vital sign signal from the radar echo according to the position of the human body object.
An embodiment of a first aspect of the present application provides a life detection method, including:
detecting by adopting an ultra-wideband radar to obtain a radar echo;
receiving pulses for the radar in the radar echo along the slow time direction, and determining a permutation entropy PE value;
determining the position of the human body object according to the PE value;
and extracting a vital sign signal from the radar echo according to the position of the human body object.
According to the life detection method, the ultra-wideband radar is adopted for detection to obtain the radar echo; receiving pulses for radar in a radar echo along a slow time direction, and determining a permutation entropy PE value; determining the position of the human body object according to the PE value; vital sign signals are extracted from the radar echoes according to the position of the human subject. Therefore, the position of the human body object can be determined according to the PE value of the radar receiving pulse in the radar echo along the slow time direction, and the vital sign signal can be effectively extracted from the radar echo according to the position of the human body object.
An embodiment of a second aspect of the present application provides a life detection apparatus, including:
the detection module is used for detecting by adopting an ultra-wideband radar to obtain a radar echo;
the first determining module is used for receiving pulses to the radar in the radar echo along the slow time direction and determining a permutation entropy PE value;
the second determining module is used for determining the position of the human body object according to the PE value;
and the extraction module is used for extracting the vital sign signals from the radar echoes according to the position of the human body object.
According to the life detection device, the ultra-wideband radar is adopted for detection to obtain the radar echo; receiving pulses for radar in a radar echo along a slow time direction, and determining a permutation entropy PE value; determining the position of the human body object according to the PE value; vital sign signals are extracted from the radar echoes according to the position of the human subject. Therefore, the position of the human body object can be determined according to the PE value of the radar receiving pulse in the radar echo along the slow time direction, and the vital sign signal can be effectively extracted from the radar echo according to the position of the human body object.
An embodiment of a third aspect of the present application provides a computer device, including: the life detection system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the life detection method as set forth in the embodiment of the first aspect of the application.
An embodiment of a fourth aspect of the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the life detection method as set forth in the embodiment of the first aspect of the present application.
An embodiment of a fifth aspect of the present application provides a computer program product, wherein when instructions of the computer program product are executed by a processor, the life detection method as set forth in the embodiment of the first aspect of the present application is performed.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a life detection method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of another life detection method provided in the embodiments of the present application;
fig. 3 is a schematic diagram of a vital sign signal vector extracted in the embodiment of the present application;
FIG. 4 is a schematic flow chart of another life detection method provided in the embodiments of the present application;
FIG. 5 is a schematic structural diagram of an ultra-wideband radar system in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a life detection device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The ultra-wideband radar has the advantages of high distance resolution, strong penetration capability, low power consumption, strong anti-interference capability and the like, so that the ultra-wideband radar can detect the position, the respiration, the heartbeat and other information of a human body in a non-contact manner and in a long distance. Because weak sign signals generated by the micromotion of the human body object are submerged in strong noise, it is important to select a proper signal processing algorithm for the extraction of respiration and heartbeat signals.
That is to say, when the pulse ultra-wideband radar non-contact detection technology is used for detecting vital sign signals, weak vital sign echo signals are often covered by various noises, so that a human body object cannot be identified and positioned.
In the related art, the radar echo signal is adaptively decomposed into an Intrinsic Mode Function (IMF) by mainly using an EMD (Empirical Mode Decomposition) algorithm, the energy spectrum characteristics of each IMF are analyzed, and the respiration and heartbeat signals are reconstructed in the time domain. In the actual underground collapse rescue, the respiration and heartbeat waveforms of the human body object can be well reconstructed by processing the radar echo signals by using the EMD algorithm, and the method has good application value.
However, the EMD algorithm has the following disadvantages:
in the first aspect, modal aliasing exists during IMF decomposition, that is, an IMF contains characteristic components with different time scales. On the one hand due to the signal itself and on the other hand a deficiency of the EMD algorithm itself;
in the second aspect, multiple iterations are required in the process of decomposing the IMF, and the condition for stopping the iterations lacks a criterion, so that different conditions for stopping the iterations result in different IMF components (IMFs).
Also, the breathing frequency and the heartbeat frequency are obtained by fourier spectrum analysis of a single frame signal. However, when the sampling frequency is fixed, the longer the time series, the better the spectrum, and therefore, long-term objective data is required, which also reduces the detection efficiency of the radar.
Therefore, in order to solve the above problems, the present application provides a life detection method for an ultra-wideband radar based on a combination of a Permutation Entropy (PE) and an Ensemble Empirical Mode Decomposition (EEMD), which determines position information of a human body object according to a PE value of a pulse received by the radar, and acquires information of respiration and heartbeat frequency by using the EEMD method, thereby reconstructing a heartbeat signal and a respiration signal.
The life detection method and apparatus according to the embodiments of the present application are described below with reference to the drawings.
Fig. 1 is a schematic flowchart of a life detection method according to an embodiment of the present disclosure.
The embodiment of the present application is exemplified by the life detection method being configured in a life detection apparatus, and the life detection apparatus can be applied to any device with computing capability, so that the device can execute a life detection function.
As shown in fig. 1, the life detection method may include the steps of:
and 101, detecting by using an ultra-wideband radar to obtain a radar echo.
In the embodiment of the application, a detection signal can be sent to a human body object through an ultra-wideband radar, and a feedback signal returned by the human body object is received, where the feedback signal is a radar echo (or referred to as a radar echo signal).
And 102, receiving pulses for the radar in the radar echo along the slow time direction, and determining a permutation entropy PE value.
In the embodiment of the application, in order to obtain the position information of the human body object in the radar echo, the statistical characteristics of the slow time direction data in the radar echo can be analyzed. Specifically, the PE value may be determined for radar acceptance pulses in the slow time direction in radar returns, so as to determine the position of the human object according to the PE value.
In a possible implementation manner of the embodiment of the application, a slow time sequence obtained by sampling a corresponding radar received pulse can be determined for a fast time point set in a fast time direction in a radar echo. For example, if the fast time point is m1 and the length of the slow time sequence is N, the slow time sequence may be: y [ m1, i]Y (1), Y (2), …, Y (i), wherein i is 1,2, 3, …, N, Y e WM×N，WM×NThe method is characterized by comprising the steps of radar echo (in a matrix form), wherein M is the number of sampling points of delay time of the radar echo, and N is the number of sampling points of scanning time.
Then, a matrix of overlapping column vectors can be generated from the slow time sequence. For example, the generated matrix of overlapping column vectors may be:
wherein, taudAnd q are two parameters set, τdFor delay time, q is the embedding dimension, K ═ N (-q-1) τd. Optionally, q may take the value 3, τdThe value of (d) may be 1.
The overlapping column vector matrix may then be reconstructed; each row in the overlapped column vector matrix is used as a reconstruction component, and elements in each reconstruction component after reconstruction are arranged according to an ascending order. For example, each row in the overlapped column vector matrix is a reconstructed vector, and for the jth reconstructed vector, the elements in the reconstructed vector may be arranged in an ascending order to obtain the reconstructed component after reconstruction. For example, the reconstructed components after reconstruction may be:
y(i+(j1-1)τd)≤y(i+(j2-1)τd)≤…≤y(i+(jq-1)τd)；
and if the elements in the reconstruction vector are equal, sorting according to the j value.
Each reconstructed component in the reconstructed overlapping column vector matrix may then be treated as a sequence and the ranking probability for each sequence determined. For example, for each reconstructed component in the reconstructed overlapping column vector matrix, the following set of sequences can be obtained:
S(l)＝(j1,j2,...,jq)；
wherein l is 1,2, …, k, k is less than q! The m-dimensional sequence yields m! S (l) sorting, the sorting probability P corresponding to each S (l) sequence can be calculatedkWherein the sum of all the ranking probabilities is 1.
And finally, determining the PE value corresponding to the slow time sequence according to the sequencing probability of each sequence. For example, the permutation entropy PE value for Y [ m1, i ] may be defined as:
wherein HpNamely the PE value.
In addition, H ispHas a value range of [0,1 ]]，HpIs a slow time sequence Y m1, i]Complexity of HpThe smaller the value of (a), the more regular the slow time series change.
And 103, determining the position of the human body object according to the PE value.
In the embodiment of the application, the position of the human body object may be a distance between the human body object and the ultra-wideband radar.
In addition, in the radar echo WM×NIn the method, the slow time direction data change of the position range of the human body object is relatively regular, so that the PE value corresponding to the area where the human body is located is low, and therefore in the method, the position of the human body object can be determined by finding the minimum value of the PE value, and the distance between the human body object and the ultra-wideband radar is determined according to the position.
Specifically, the minimum PE value may be determined from the PE values of the slow time sequence corresponding to each fast time point, and the target sampling point P in the fast time direction to which the minimum PE value belongsposThe distance between the human body object and the ultra-wideband radar is determined, for example, the distance range between the human body object and the ultra-wideband radar may be determined according to the following formula:
wherein v is 3 × 108m/s，TfIndicating the sampling interval in the fast time direction.
And 104, extracting the vital sign signals from the radar echo according to the position of the human body object.
In an embodiment of the present application, the vital sign signals may include a heartbeat signal and a respiration signal.
In the embodiment of the application, after the position of the human body object is determined, the vital sign signal can be extracted from the radar echo according to the position of the human body object.
According to the life detection method, the ultra-wideband radar is adopted for detection to obtain the radar echo; receiving pulses for radar in a radar echo along a slow time direction, and determining a permutation entropy PE value; determining the position of the human body object according to the PE value; vital sign signals are extracted from the radar echoes according to the position of the human subject. Therefore, the position of the human body object can be determined according to the PE value of the radar receiving pulse in the radar echo along the slow time direction, and the vital sign signal can be effectively extracted from the radar echo according to the position of the human body object.
In order to clearly illustrate how the vital sign signals are extracted from the radar echo in the present application, another life detection method is provided in the present embodiment, and fig. 2 is a flowchart of another life detection method provided in the present embodiment.
As shown in fig. 2, the life detection method may include the steps of:
and step 201, detecting by using an ultra-wideband radar to obtain a radar echo.
It should be noted that, in an actual detection scene of the ultra-wideband radar, due to direct coupling of the radar antenna, and reflection and scattering of radar waves by a surrounding environment, a radar echo obtained by the ultra-wideband radar detection may have strong background clutter. Background clutter typically manifests as direct or low frequency components, linear trends, etc. Therefore, in the embodiment of the present application, before the vital sign signal is extracted from the radar echo, the radar echo may be preprocessed to eliminate the background clutter in the radar echo, so as to improve the signal-to-noise ratio.
As an example, after acquiring the radar echo, the radar echo may be pre-processed by at least one of:
firstly, a track signal Subtraction (RPS) method is used to remove background waves from radar echoes to remove constant components in the radar echoes.
For example, for radar returns WM×NAssume that the fast time sequence is YjJ is 1,2, 3, …, N is the number of sampling points in the scanning time, and the result Y' of subtracting trace signals is:
Yj'＝Yj-Yj-1；
secondly, a Time Mean Subtraction (TMS) method is used to remove background waves from the radar echo, so as to remove the constant components in the radar echo.
That is, in the present application, the radar echo component of the stationary object can be estimated using TMS, and this component approximates the Direct Current (DC) component
Comprises the following steps:
thus, the radar echo after eliminating the constant component can be:
thirdly, background wave elimination is carried out on the radar echo signal by adopting a linear trend inhibition method so as to eliminate constant components in the radar echo.
For example, the radar echo after the constant component is eliminated can be determined by the following formula:
W＝TT-X(XTX)-1XTTT；
where W is a radar echo after the constant component is removed, T is a radar echo before the constant component is not removed, and X ═ X1,x2]，x1＝[0,1,...,N-1]T，
And fourthly, performing gain control on the radar echo to enhance the vital sign components in the radar echo.
That is, in the present application, an automatic Gain Control (AGC for short) may be used to enhance the weak vital sign signals in the slow time direction, further improve the signal-to-noise ratio, and calculate the corresponding Gain coefficient according to the energy in the selected time window 2 λ +1, thereby implementing adaptive Control.
By the n-th1Radar echo r (tau, n) of a frame1) For example, the gain factor gmaskCan be as follows:
then the nth after gain control1Radar echo r of a frameE(τ, t) is: r isE(τ,t)＝gmask(τ,t)×r(τ,t)。
And fifthly, filtering noise components in the radar echo by using a Butterworth filter.
It should be noted that, because the respiratory frequency range of the human body object is between 0.1 to 0.8Hz, and the heartbeat frequency range is between 0.8 to 2.5Hz, the butterworth filter can be used to filter the high-frequency noise signals in the radar echo. Wherein the squared magnitude function of the butterworth filter is defined as:
where N is the order of the Butterworth filter, ωcWith a 3dB cut-off frequency, epsilon is a parameter controlling the amplitude of the band-pass ripple, and the selected frequency band can be 0.1-2.5 Hz.
In a possible implementation manner of the embodiment of the present application, a Generalized Cross Validation function (GCV) may also be used to determine the threshold value independent of the real data and the noise energy, and it is not necessary to rely on the input and output data. For example, a threshold value that is independent of true data and noise energy may be determined according to the following equation:
wherein N and N0Respectively representing the number of wavelet coefficientsAnd the number of wavelet coefficients of 0, w and wtRespectively representing radar echoes containing noise and after noise reduction.
And step 203, determining the position of the human body object according to the PE value.
The execution process of steps 201 to 203 may refer to the execution process of steps 101 to 103 in the above embodiments, which is not described herein again.
In an embodiment of the present application, the lateral distance of the human body may be a lateral distance of the human thorax.
For example, assume that the lateral distance of the human thorax is DthoThe sampling interval in the fast time direction is marked as TfThe number P of sampling points occupied by the transverse distance of the human thorax in the radar receiving pulsethoComprises the following steps:
wherein v is 3 × 108m/s。
For example, the target echo signal may be determined according to the following formula:
Ψ＝Ω[(Ppos-Ptho/2)：(Ppos+Ptho/2)，1：n]；
wherein, P is more than or equal to 0pos，PthoM ≦ N ═ 1, 2., N, Ω are radar echoes, Ψ is the target echo signal.
In an embodiment of the present application, a vital sign signal vector may be extracted from a target echo signal.
As an example, by sampling the target sample point Ppos(characterizing the position of the human subject) the radar echoes of adjacent range gates are arranged in a row and recombined, and the obtained vital sign signal vector ζ can be as shown in fig. 3.
The vital sign signals of the human subject are distributed over adjacent range gates due to the following properties: 1. the radar emission has a certain track; 2. because the transverse distance of the chest of the human body is close to 30-40cm, a plurality of radar scattering points can exist in the chest at the same time; 3. the human subject has a slight swing. Selecting and recombining signals on these neighboring range gates based on PE values can obtain more vital sign information than a single frame signal and can reduce observation time.
In order to clearly illustrate how the vital sign signal vector is extracted from the target echo signal in the present application, another method for detecting a vital sign is provided in the present embodiment, and fig. 4 is a schematic flowchart of another method for detecting a vital sign provided in the present embodiment.
As shown in fig. 4, the life detection method may include the steps of:
and step 401, detecting by using an ultra-wideband radar to obtain a radar echo.
And step 402, receiving pulses of the radar along the slow time direction in the radar echo, and determining a permutation entropy PE value.
And step 403, determining the position of the human body object according to the PE value.
The execution process of steps 401 to 405 may refer to the execution process of any embodiment of the present application, and is not described herein again.
In this embodiment, the target echo signal is marked as x (t), and in order to suppress mixing between IMF components, at least one round of loop process may be performed on x (t), where each round of loop process includes: and adding white noise to the target echo signal, and performing signal decomposition on the target echo signal added with the white noise to obtain a plurality of IMF components and residual components.
For example, the number of execution rounds of the labeling loop process is M, where M is a positive integer, and for the ith round of the loop process, where i ═ 1, 2.
xi(t)＝x(t)+ni(t)；
Can convert x intoi(t) EMD decomposition to give:
wherein n is the number of EMD decomposed IMFs, ci,n(t) are IMF components (IMFs), ri,n(t) is the residual component.
And repeatedly executing M circulation processes, and adding white noise with different amplitudes in each circulation process to obtain a series of IMFs.
In the embodiment of the present application, the IMF component in each round of the loop process may be averaged, and the average may be used as the target IMF component. That is, the average value of the IMFs in each round-robin process can be used as the IMF component c of the EEMDn(t) in the present application, cn(t) is noted as the target IMF component. For example, the target IMF component c may be determined according to the following equationn(t)：
Wherein, ci,n(t) is the IMF component in the ith round-robin process, i.e. the IMF component in each round-robin process is:
[{c1,n(t)},{c2,n(t)},...,{cM,n(t)}]；
wherein N is 1, 2.. times.n; 1, 2. It should be noted that the number M of execution rounds of the loop process is a key parameter of the EEMD algorithm, in the present application, white gaussian noise may be added to be 0.2 times of the standard deviation of x (t), the g.ringing criterion is the stopping criterion of the EEMD algorithm, through multiple experiments, M may be set to be 50 times, and the structure diagram of the ultra wideband radar system adopted in the experiment may be as shown in fig. 5. The FPGA is a Field Programmable Gate Array (Field Programmable Gate Array), the ARM is a microprocessor, and the ADC is an Analog-to-Digital Converter (ADC).
And step 408, reconstructing the vital sign signal vector according to the target IMF component.
In an embodiment of the present application, a vital sign signal vector may be reconstructed from the target IMF component.
It should be noted that important components of the vital sign signals can be concentrated in a low frequency range, wherein a distribution range of the heartbeat frequency is 1 to 2.5Hz, and a distribution range of the respiratory frequency is 0.2 to 0.8 Hz. The respiration signal and the heartbeat signal can thus be reconstructed using the portion of the IMFs within the spectral range of the target echo signal.
In a possible implementation manner of the embodiment of the present application, the target IMF component may be decomposed to obtain a corresponding set of index pattern sets { z }1,z2,z3,z4Is epsilon with Z; reconstructing the vital sign signal according to the elements in the index mode set corresponding to the target IMF component; wherein the vital sign signals include a heartbeat signal and a respiration signal.
For example, each target IMF component may be decomposed to obtain an index pattern set { z } corresponding to each target IMF component1,z2,z3,z4And reconstructing according to z1 and z2 corresponding to each target IMF component to obtain a heartbeat signal, and reconstructing according to z3 and z4 corresponding to each target IMF component to obtain a respiration signal.
In a possible implementation manner of the embodiment of the present application, before the vital sign signal is reconstructed, a first energy ratio may be determined according to the total energy of each target IMF component in the frequency domain and the first energy in the respiratory frequency distribution range, a second energy ratio may be determined according to the total energy of each target IMF component in the frequency domain and the second energy in the cardiac frequency distribution range, whether the first energy ratio and the second energy ratio of each target IMF component meet a determination condition is determined, if the first energy ratio and the second energy ratio of each target IMF component meet the determination condition, it is determined that the vital sign signal can be reconstructed according to the target IMF component, and if the first energy ratio and the second energy ratio of each target IMF component do not meet the determination condition, it is determined that the vital sign signal cannot be reconstructed.
For example, for each target IMF component, the target IMF component may be decomposed into ZIMFS, resulting in a set of index pattern sets { z }1,z2,z3,z4Is equal to Z. Wherein, index pattern set { z1,z2,z3,z4The selection criterion is that Fourier transform is carried out on each eigenmode function, and the first energy E of each target IMF component in the total energy E (f) and the respiratory frequency distribution range (0.2-0.8Hz) of the frequency domain is calculatedr(f) A second energy E in the range of the heart beat frequency distribution (1-2.5Hz)h(f) In that respect When the first energy ratio is Er(f) E (f) and a second energy ratio Eh(f) (e) (f) determining that the vital sign signal can be reconstructed from the target IMF component when the following formula is satisfied:
e.g. heartbeat signal sh(t) may be represented by z2-z1+1IMFs reconstruction, the respiratory signal sr(t) may be represented by z4-z3+1IMFs reconstruction, i.e.:
wherein, deltar，δhIs an energy ratio threshold corresponding to the respiration signal and the heartbeat signal, respectively, which is related to the posture of the human subject, and the energy ratio threshold may be 0.8 when the human subject is close to the ultra-wideband radar.
In summary, the distance between the human object and the ultra-wideband radar can be estimated by calculating the PE value of the radar acceptance pulse in the slow time direction. Also, vital sign signals of the human subject are distributed over adjacent range gates due to the following characteristics: 1. the radar emission has a certain track; 2. because the transverse distance of the chest of the human body is close to 30-40cm, a plurality of radar scattering points can exist in the chest at the same time; 3. the human subject has a slight swing. Selecting and recombining signals on these neighboring range gates based on PE values can obtain more vital sign information than a single frame signal and can reduce observation time. And decomposing the combined radar echo into IMFs adaptively by adopting an EEMD algorithm, and reconstructing a respiratory signal and a heartbeat signal according to the energy ratio of each IMF in respiratory and heartbeat frequency bands. And finally, calculating fourth-order cumulant of the reconstructed respiration and heartbeat signals, and carrying out FFT (fast Fourier transform) on the cumulant, thereby obtaining the frequency of respiration and heartbeat, overcoming the modal aliasing problem of EMD (empirical mode decomposition), and being capable of accurately and effectively extracting the distance and vital sign information of a human body object. The method is an ultra-wideband radar life detection method based on the combination of the generalized cross threshold, the permutation entropy and the ensemble empirical mode decomposition, and can extract the distance and the vital sign information of the human body object more accurately and effectively.
Corresponding to the life detection method provided in the embodiments of fig. 1 to 4, the present application also provides a life detection device, and since the life detection device provided in the embodiments of the present application corresponds to the life detection method provided in the embodiments of fig. 1 to 4, the implementation of the life detection method is also applicable to the life detection device provided in the embodiments of the present application, and will not be described in detail in the embodiments of the present application.
Fig. 6 is a schematic structural diagram of a life detection device according to an embodiment of the present application.
As shown in fig. 6, the life detection apparatus 600 may include: a detection module 610, a first determination module 620, a second determination module 630, and an extraction module 640.
And the detection module 610 is used for detecting and obtaining radar echo by adopting an ultra-wideband radar.
The first determining module 620 is configured to determine a permutation entropy PE value for a radar acceptance pulse in a radar echo along a slow time direction.
A second determining module 630, configured to determine the position of the human object according to the PE value.
And an extracting module 640, configured to extract the vital sign signal from the radar echo according to the position of the human object.
Further, in a possible implementation manner of the embodiment of the present application, the life detection apparatus 600 may further include:
the first processing module is used for eliminating background waves of radar echoes by adopting a channel signal subtraction method so as to eliminate constant components in the radar echoes; and/or, adopting a time subtraction method to eliminate background waves of the radar echoes so as to eliminate constant components in the radar echoes; and/or performing background wave elimination on the radar echo signal by adopting a linear trend suppression method to eliminate constant components in the radar echo.
Further, in a possible implementation manner of the embodiment of the present application, the life detection apparatus 600 may further include:
the second processing module is used for performing gain control on the radar echo so as to enhance the vital sign components in the radar echo; and/or filtering noise components in the radar echo by using a Butterworth filter.
Further, in a possible implementation manner of the embodiment of the present application, the first determining module 620 is specifically configured to: determining a slow time sequence obtained by sampling a corresponding radar received pulse for a fast time point set in the fast time direction in the radar echo; generating an overlapped column vector matrix according to the slow time sequence; reconstructing the overlapped column vector matrix; each row in the overlapped column vector matrix is used as a reconstruction component, and elements in each reconstructed component after reconstruction are arranged according to an ascending order; taking each reconstructed component in the reconstructed overlapped column vector matrix as a sequence, and determining the sequencing probability of each sequence; and determining the PE value corresponding to the slow time sequence according to the sequencing probability of each sequence.
Further, in a possible implementation manner of the embodiment of the present application, the second determining module 630 is specifically configured to: determining a minimum PE value from the PE values of the slow time sequence corresponding to the fast time points; according to the target sampling point P in the fast time direction to which the minimum PE value belongsposAnd determining the distance between the human body object and the ultra-wideband radar.
Further, in a possible implementation manner of the embodiment of the present application, the extracting module 640 is specifically configured to: according to the set human body transverse distance and the sampling interval in the fast time direction, determining the number P of sampling points occupied by the human body transverse distance in the fast time directiontho(ii) a According to the number P of sampling pointsthoAnd a target sampling point PposDetermining a target echo signal from the radar echo; and extracting a vital sign signal vector from the target echo signal.
Further, in a possible implementation manner of the embodiment of the present application, the extracting module 640 is specifically configured to: executing at least one round of circulation process, wherein the circulation process comprises the following steps: adding white noise to the target echo signal, and performing signal decomposition on the target echo signal added with the white noise to obtain a plurality of IMF components and residual components; wherein, in each round of circulation process, the added white noise amplitude is different; obtaining a target IMF component according to the corresponding IMF average in each round circulation process; and reconstructing a vital sign signal vector according to the target IMF component.
Further, in a possible implementation manner of the embodiment of the present application, the extracting module 640 is specifically configured to: decomposing the target IMF components to obtain a corresponding group of index mode sets; reconstructing the vital sign signal according to the elements in the index mode set corresponding to the target IMF component; wherein the vital sign signals include a heartbeat signal and a respiration signal.
Further, in a possible implementation manner of the embodiment of the present application, the extracting module 640 is further configured to: determining a first energy ratio according to the total energy of each target IMF component in the frequency domain and the first energy in the respiratory frequency distribution range, and determining a second energy ratio according to the total energy of each target IMF component in the frequency domain and the second energy in the heartbeat frequency distribution range; and determining that the first energy ratio and the second energy ratio of each target IMF component meet the determination condition.
According to the life detection device, the ultra-wideband radar is adopted for detection to obtain the radar echo; receiving pulses for radar in a radar echo along a slow time direction, and determining a permutation entropy PE value; determining the position of the human body object according to the PE value; vital sign signals are extracted from the radar echoes according to the position of the human subject. Therefore, the position of the human body object can be determined according to the PE value of the radar receiving pulse in the radar echo along the slow time direction, and the vital sign signal can be effectively extracted from the radar echo according to the position of the human body object.
In order to implement the foregoing embodiments, the present application also provides a computer device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the program, implementing the life detection method as set forth in any of the preceding embodiments of the present application.
In order to achieve the above embodiments, the present application also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a life detection method as proposed in any of the previous embodiments of the present application.
In order to implement the above embodiments, the present application also proposes a computer program product, wherein instructions of the computer program product, when executed by a processor, perform the life detection method as proposed in any of the previous embodiments of the present application.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method of life detection, comprising the steps of:
detecting by adopting an ultra-wideband radar to obtain a radar echo;
receiving pulses for the radar in the radar echo along the slow time direction, and determining a permutation entropy PE value;
determining the position of the human body object according to the PE value;
and extracting a vital sign signal from the radar echo according to the position of the human body object.
2. The method of claim 1, further comprising:
background wave elimination is carried out on the radar echo by adopting a channel signal subtraction method so as to eliminate constant components in the radar echo;
and/or, performing background wave elimination on the radar echo by adopting a time subtraction method so as to eliminate constant components in the radar echo;
and/or performing background wave elimination on the radar echo signal by adopting a linear trend suppression method so as to eliminate constant components in the radar echo.
3. The method of claim 1, further comprising:
performing gain control on radar echo to enhance vital sign components in the radar echo;
and/or filtering noise components in the radar echo by using a Butterworth filter.
4. The method according to any one of claims 1 to 3, wherein the determining a permutation entropy PE value for the radar acceptance pulse in the slow time direction in the radar echo comprises:
determining a slow time sequence obtained by sampling a corresponding radar received pulse for a fast time point set in the fast time direction in the radar echo;
generating an overlapped column vector matrix according to the slow time sequence;
reconstructing the overlapping column vector matrix; each row in the overlapped column vector matrix is used as a reconstruction component, and elements in each reconstructed component after reconstruction are arranged according to an ascending order;
taking each reconstructed component in the reconstructed overlapped column vector matrix as a sequence, and determining the sequencing probability of each sequence;
and determining the PE value corresponding to the slow time sequence according to the sequencing probability of each sequence.
5. The method of claim 4, wherein determining the location of the human subject based on the PE values comprises:
determining a minimum PE value from the PE values of the slow time sequence corresponding to the fast time points;
according to the target sampling point P in the fast time direction to which the minimum PE value belongsposAnd determining the distance between the human body object and the ultra-wideband radar.
6. The method according to claim 5, wherein said extracting vital sign signal vectors from radar echoes according to the position of the human subject comprises:
determining the number P of sampling points occupied by the human body transverse distance in the fast time direction according to the set human body transverse distance and the sampling interval in the fast time directiontho；
According to the number P of the sampling pointsthoAnd a target sampling point PposDetermining a target echo signal from the radar echo;
extracting the vital sign signal vector from the target echo signal.
7. The method of claim 6, wherein said extracting the vital sign signal vector from the target echo signal comprises:
executing at least one round of circulation process, wherein the circulation process comprises the following steps: adding white noise to the target echo signal, and performing signal decomposition on the target echo signal added with the white noise to obtain a plurality of IMF components and residual components; wherein, in each round of circulation process, the added white noise amplitude is different;
obtaining a target IMF component according to the corresponding IMF average in each round circulation process;
and reconstructing a vital sign signal vector according to the target IMF component.
8. The method according to claim 7, wherein reconstructing vital sign signals from the target IMF components comprises:
decomposing the target IMF components to obtain a group of corresponding index mode sets;
reconstructing a vital sign signal according to the element in the index mode set corresponding to the target IMF component; wherein the vital sign signals comprise a heartbeat signal and a respiration signal.
9. The method according to claim 8, wherein before reconstructing the vital sign signal from the target IDM component and the elements in the index pattern set, further comprising:
determining a first energy ratio according to the total energy of each target IMF component in the frequency domain and the first energy in the respiratory frequency distribution range, and determining a second energy ratio according to the total energy of each target IMF component in the frequency domain and the second energy in the heartbeat frequency distribution range;
determining that the first energy ratio and the second energy ratio of each target IMF component meet a determination condition.
10. A life detection device, comprising:
the detection module is used for detecting by adopting an ultra-wideband radar to obtain a radar echo;
the first determining module is used for receiving pulses to the radar in the radar echo along the slow time direction and determining a permutation entropy PE value;
the second determining module is used for determining the position of the human body object according to the PE value;
and the extraction module is used for extracting the vital sign signals from the radar echoes according to the position of the human body object.
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