A method and system for detecting life information based on singular spectrum decomposition algorithm

Abstract

The present application proposes a method and system for detecting life information based on a singular spectrum decomposition algorithm, the method comprising: using an ultra-wideband radar to detect an area to be detected, obtaining a radar echo matrix corresponding to the area to be detected, and filtering out background clutter in the radar echo matrix; extracting vital signs signals of human targets in the area to be detected according to the radar echo matrix after filtering out clutter; using a singular spectrum decomposition algorithm to decompose the vital signs signals to obtain the singular spectrum components corresponding to the vital signs signals; reconstructing the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands; Fourier transforming the reconstructed breathing and heartbeat signals respectively to obtain the life information of the human target. The technical solution provided by the present invention can accurately and quickly detect the life information of human targets based on the singular spectrum decomposition algorithm.

Description

Technical Field

[0001] The present application relates to the technical field of life information detection, and in particular to a life information detection method and system based on a singular spectrum decomposition algorithm. Background Art

[0002] Life detection radar is a high-tech life-saving device developed by integrating micro-power ultra-wideband radar technology and biomedical engineering technology. It is professionally used for searching and locating survivors in emergency rescue missions such as earthquake disasters and landslides. It uses the characteristics of nanosecond electromagnetic wave pulses, such as wide spectrum, strong penetration, high resolution, good anti-interference, and low power consumption, to penetrate non-metallic media and emit nanosecond pulse electromagnetic waves to long distances. Based on the time domain Doppler effect generated by human movement on the radar echo, it analyzes whether there is a human target in the detection range and obtains the specific location and vital signs information of the human target.

[0003] The existing detection of respiratory frequency and heart rate in life information is obtained by Fourier spectrum analysis of single frame signal. However, when the sampling frequency is fixed, the longer the time series, the better the spectrum, so long-term objective data is required, but the efficiency of the radar is reduced. In addition, most of the methods derived from improved Empirical Mode Decomposition (EMD), such as Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition of Adaptive Noise (CEEMDAN) and their derivation by dividing high and low frequency components to reduce noise, only target the high frequency components in the components for noise reduction and ignore the small amount of noise in the low frequency components. Such low frequency components are directly used for reconstruction, which will seriously affect the frequency estimation accuracy, thereby making the accuracy of life information detection low. Summary of the invention

[0004] The present application provides a method and system for detecting life information based on a singular spectrum decomposition algorithm, so as to at least solve the technical problem of low accuracy of life information detection in related technologies.

[0005] The first embodiment of the present application proposes a life information detection method based on a singular spectrum decomposition algorithm, the method comprising:

[0006] Using an ultra-wideband radar to detect the area to be detected, obtaining a radar echo matrix corresponding to the area to be detected, and filtering out background clutter in the radar echo matrix;

[0007] Extract the vital sign signals of human targets in the area to be detected according to the radar echo matrix after filtering out clutter;

[0008] Decomposing the vital sign signal by using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal;

[0009] Reconstruct the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands;

[0010] The reconstructed breathing and heartbeat signals are respectively subjected to Fourier transformation to obtain the life information of the human target.

[0011] Preferably, the step of extracting the vital sign signal of the human target in the area to be detected according to the radar echo matrix after filtering out the clutter includes:

[0012] Calculating the sample entropy of the slow time series corresponding to each range gate in the radar echo matrix after filtering out clutter and the number of points occupied by the chest distance of the human target in the received pulse;

[0013] The range gate corresponding to the minimum value of the sample entropy is used as the range gate where the human target is located, and the position of the human target is determined based on the range gate where the human target is located;

[0014] Determine the adjacent range gate of the range gate corresponding to the position of the human target according to the number of points occupied by the position and the chest distance of the human target in the received pulse;

[0015] The echo signal of the range gate corresponding to the position of the human target and the echo signals of the adjacent range gates are extracted, and the extracted echo signals are reorganized and arranged in rows to obtain the vital sign signal of the human target.

[0016] Preferably, the step of decomposing the vital sign signal using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal includes:

[0017] Decomposing the vital sign signal into the sum of m singular spectrum components and a trend term using a singular spectrum decomposition algorithm;

[0018] Wherein, the m is determined based on the number of iterations in the process of decomposing the vital sign signal.

[0019] Preferably, reconstructing the respiratory and heartbeat signals according to the energy percentage of each singular spectral component in the respiratory and heartbeat frequency bands includes:

[0020] respectively determining the energy percentage of each singular spectral component in the respiratory frequency band and the energy percentage of each singular spectral component in the heartbeat frequency band;

[0021] Selecting singular spectrum components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold and singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold in each singular spectrum;

[0022] reconstructing a respiratory signal based on singular spectral components whose energy percentage within the respiratory frequency band is greater than a preset respiratory energy ratio threshold;

[0023] The heartbeat signal is reconstructed based on the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold.

[0024] Furthermore, the reconstructing the respiratory signal based on the singular spectral component whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold comprises:

[0025] Determine the sum of the singular spectral components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold, and use the sum of the singular spectral components as a reconstructed respiratory signal;

[0026] The reconstructing the heartbeat signal based on the singular spectral component whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold comprises:

[0027] The sum of the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold is determined, and the sum of the singular spectral components is used as the reconstructed heartbeat signal.

[0028] The second embodiment of the present application proposes a life information detection system based on a singular spectrum decomposition algorithm, comprising:

[0029] A filtering module is used to detect the area to be detected using an ultra-wideband radar, obtain a radar echo matrix corresponding to the area to be detected, and filter out background clutter in the radar echo matrix;

[0030] An extraction module is used to extract the vital sign signals of human targets in the area to be detected based on the radar echo matrix after clutter is filtered out;

[0031] A decomposition module, used to decompose the vital sign signal using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal;

[0032] A reconstruction module, used for reconstructing the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands;

[0033] The transformation module is used to perform Fourier transformation on the reconstructed breathing and heartbeat signals respectively to obtain the life information of the human target.

[0034] Preferably, the extraction module comprises:

[0035] A calculation unit, used for calculating the sample entropy of the slow time series corresponding to each range gate in the radar echo matrix after filtering out clutter and the number of points occupied by the chest distance of the human target in the received pulse;

[0036] A first determining unit is used to use the range gate corresponding to the minimum value of sample entropy as the range gate where the human target is located, and determine the position of the human target based on the range gate where the human target is located;

[0037] A second determining unit is used to determine the adjacent range gate of the range gate corresponding to the position of the human target according to the number of points occupied by the position and the chest distance of the human target in the received pulse;

[0038] The extraction unit is used to extract the echo signal of the range gate corresponding to the position of the human target and the echo signal of the adjacent range gate, and reorganize and arrange the extracted echo signals into rows to obtain the vital sign signal of the human target.

[0039] Preferably, the decomposition module is used to:

[0040] Decomposing the vital sign signal into the sum of m singular spectrum components and a trend term using a singular spectrum decomposition algorithm;

[0041] Wherein, the m is determined based on the number of iterations in the process of decomposing the vital sign signal.

[0042] Preferably, the reconstruction module comprises:

[0043] A third determining unit is used to respectively determine the energy percentage of each singular spectral component in the respiratory frequency band and the energy percentage of each singular spectral component in the heartbeat frequency band;

[0044] A screening unit, used to screen out singular spectrum components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold and singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold in each singular spectrum;

[0045] A first reconstruction unit, configured to reconstruct a respiratory signal based on a singular spectral component whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold;

[0046] The second reconstruction unit is used to reconstruct the heartbeat signal based on the singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold.

[0047] Furthermore, the first reconstruction unit is used to:

[0048] Determine the sum of the singular spectral components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold, and use the sum of the singular spectral components as a reconstructed respiratory signal;

[0049] The second reconstruction unit is used for:

[0050] The sum of the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold is determined, and the sum of the singular spectral components is used as the reconstructed heartbeat signal.

[0051] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0052] The present application proposes a method and system for detecting life information based on a singular spectrum decomposition algorithm, the method comprising: using an ultra-wideband radar to detect an area to be detected, obtaining a radar echo matrix corresponding to the area to be detected, and filtering out background clutter in the radar echo matrix; extracting vital signs signals of human targets in the area to be detected according to the radar echo matrix after filtering out clutter; using a singular spectrum decomposition algorithm to decompose the vital signs signals to obtain the singular spectrum components corresponding to the vital signs signals; reconstructing the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands; Fourier transforming the reconstructed breathing and heartbeat signals respectively to obtain the life information of the human target. The technical solution provided by the present invention can accurately and quickly detect the life information of human targets based on the singular spectrum decomposition algorithm.

[0053] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0055] Figure 1 A schematic diagram of a flow chart of a method for detecting life information based on a singular spectrum decomposition algorithm according to an embodiment of the present application;

[0056] Figure 2 This is a structural diagram of a life information detection system based on a singular spectrum decomposition algorithm provided according to an embodiment of the present application.

[0057] Figure 3 A structural diagram of an extraction module in a life information detection system based on a singular spectrum decomposition algorithm provided according to an embodiment of the present application;

[0058] Figure 4This is a structural diagram of a reconstruction module in a life information detection system based on a singular spectrum decomposition algorithm according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0060] The following describes a method and system for detecting life information based on a singular spectrum decomposition algorithm according to an embodiment of the present application with reference to the accompanying drawings.

[0061] Example 1

[0062] Figure 1 The flowchart of a method for detecting life information based on a singular spectrum decomposition algorithm according to an embodiment of the present application is as follows: Figure 1 As shown, it may include:

[0063] Step 1: Use an ultra-wideband radar to detect the area to be detected, obtain a radar echo matrix corresponding to the area to be detected, and filter out background clutter in the radar echo matrix;

[0064] The calculation formula of the radar echo matrix may be:

[0065]

[0066] Where, τ = mT f ,m=1,2,3,…,M,M∈Z+,T f is the sampling interval in the fast time direction; t = nT s , n=1,2,…,N,N∈Z+,Z+ is a positive integer, T s is the sampling interval in the slow time direction, τ and t represent the fast time and slow time in the radar scanning data respectively, A v is the amplitude of the target human body micro-motion echo.

[0067] It should be noted that the background clutter in the radar echo matrix is ​​eliminated by using a channel signal subtraction method, a time flattening method, a linear trend suppression method, an automatic gain control method, or a slow time filtering method.

[0068] Step 2: Extract the vital sign signals of human targets in the area to be detected based on the radar echo matrix after filtering out clutter;

[0069] In the embodiment of the present disclosure, step 2 includes:

[0070] Step 2-1: Calculate the sample entropy of the slow time series corresponding to each range gate in the radar echo matrix after filtering out clutter and the number of points occupied by the chest distance of the human target in the received pulse;

[0071] The calculation of the sample entropy is as follows:

[0072] Assume that the fast time m1 is a slow time series of length N, X[m1,i]={y(1),y(2),…,y(i)}, i=1,2,3,…,N,Y∈R M×N , a set of vector sequences of dimension q are formed according to the sequence number, Yq(1), ..., Yq(N-q+1), where Yq(i) = {y(i), y(i+1), ..., y(i+q-1)}, 1≤i≤N-q+1, and these vectors represent the q consecutive values ​​of y starting from the i-th one. The distance d[Yq(i), Yq(j)] between vectors Yq(i) and Yq(j) is defined as the absolute value of the maximum difference between the corresponding elements of the two, that is, d[Y q (i),Y q (j)]=max k＝0,…,m-1 (|x(i+k)-x(j+k)|), for a given Yq(i), count the number of j (1≤j≤Nq, j≠i) whose distance between Yq(i) and Yq(j) is less than or equal to r, and record it as Bi. For 1≤i≤Nq, define: Definition B (q) (r) is: Increase the dimension to q+1 and count Y q+1 (i) and Y q+1 The number of j (1≤j≤Nq, j≠i) whose distance between them is less than or equal to r, and is recorded as A i For 1≤i≤Nq, define: Definition A (q) (r) is: So, B (q) (r) is the probability of matching q points between two sequences under similar content r, and A (q) (r) is the probability that the two sequences match at point q+1. Sample entropy is defined as: When q is a finite value, it can be estimated using the following formula: The value range of sample entropy (SampEn) is [0,1]. The size of SampEn represents the complexity of the slow time series X[m1,i]. The smaller the value of SampEn, the more regular the time series changes; conversely, the more random the time series changes.

[0073] It should be noted that the calculation formula for the number of points occupied by the chest distance of the human target in the received pulse can be:

[0074] P tho =2D tho / vT f

[0075] Where P tho is the number of points occupied by the chest distance of the human target in the received pulse, D tho is the lateral distance of the chest cavity of the human target, v = 3 × 10 8 m / s,T f is the sampling interval in the fast time direction.

[0076] Step 2-2: taking the range gate corresponding to the minimum value of sample entropy as the range gate where the human target is located, and determining the location of the human target based on the range gate where the human target is located;

[0077] It should be noted that according to the distance gate P where the human target is located pos , using the formula The distance of the human target is estimated, where Range is the distance between the human target and the radar antenna. v is the electromagnetic wave propagation speed, which is equal to 3×10 8 m / s,T f is the fast time sampling interval.

[0078] Step 2-3: determining the adjacent range gate of the range gate corresponding to the position of the human target according to the number of points occupied by the position and the chest distance of the human target in the received pulse;

[0079] It should be noted that the P corresponding to the range gate at the location of the human target is selected. tho The adjacent range gates are adjacent range gates of the range gate corresponding to the determined position of the human target.

[0080] Step 2-4: extracting the echo signal of the range gate corresponding to the position of the human target and the echo signals of the adjacent range gates, and reorganizing and arranging the extracted echo signals into rows to obtain the vital sign signal of the human target.

[0081] Step 3: Decompose the vital sign signal using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal;

[0082] In the embodiment of the present disclosure, step 3 includes:

[0083] Decomposing the vital sign signal into the sum of m singular spectrum components and a trend term using a singular spectrum decomposition algorithm;

[0084] Wherein, the m is determined based on the number of iterations in the process of decomposing the vital sign signal.

[0085] Specifically, the singular spectrum decomposition algorithm decomposes the vital sign signal into the sum of m singular spectrum components and a trend term, including:

[0086] (1) Construct a new trajectory matrix: Set the embedding dimension and data length of the vital sign signal vector ζ(t) to M and N respectively, and construct a matrix X with M rows and N columns. The i-th row of the matrix X is represented by ζ i (ζ(i),…ζ(N),ζ(1),…,ζ(i-1)), i=1,…M, matrix X is: Move the lower right corner element of the X matrix to the upper left corner to obtain the new trajectory function X(N×K), where K=N-M+1.

[0087] (2) Adaptively select the size of the embedding dimension M: Calculate the residual component V at the jth iteration j (n) Power Spectral Density (PSD), residual component: And estimate the frequency f corresponding to the maximum peak of the PSD max In the first iteration, if f max / f s <10 -3 (f s is the sampling frequency, 10 -3 is the threshold), M is set to N / 3, and the residual signal can be regarded as a trend component. When the number of iterations is greater than 1, the embedding dimension is set to M = 1.2×(f s / f max ), which is 1.2 times the component period of separation, is conducive to improving the SSA identification ability and ensuring that SSD fully plays the role of adaptive selection spectrum filter.

[0088] (3) Reconstruct the singular spurious components after j iterations: When the number of iterations j = 1, a large trend term is detected from the signal, and the matrix is ​​obtained through the first left and right eigenvectors Diagonally bisect the matrix to obtain the signal component g (1) (n). When the number of iterations j>1, the time scale component g (j) (n) The frequency components are concentrated in the interval [f max -Δf,f max +Δf] where Δf represents the residual signal v (j) (n) is half of the main peak bandwidth. max -Δf,f max The numbers of the main vectors with prominent spectral peaks in [+Δf] are established as a subset I j =(I j ={i1, ..., i p}), and then use these principal components to construct a matrix Finally, the matrix Use the diagonal averaging method to obtain the corresponding SSC signal component g (j) (n).

[0089] (4) Signal decomposition iteration stop condition setting: Each iterative decomposition obtains a new singular spectrum signal component g (j) (n), a new margin signal will also be obtained: v (j+1) (n) = v (j) (n)-g (j) (n), after j iterations, the normalized mean square error (NMSE) between the residual signal and the original signal is calculated as follows: When the normalized mean square error NMSE is less than a given threshold (th = 1%), the iterative decomposition ends; otherwise, the residual signal v (j) (n) is regarded as a new input signal and the decomposition process continues until the condition is met and the iteration stops. Assuming there are m singular spectral components, the decomposition result of SSD is:

[0090] Step 4: Reconstruct the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands;

[0091] In the embodiment of the present disclosure, step 4 includes:

[0092] respectively determining the energy percentage of each singular spectral component in the respiratory frequency band and the energy percentage of each singular spectral component in the heartbeat frequency band;

[0093] Selecting singular spectrum components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold and singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold in each singular spectrum;

[0094] reconstructing a respiratory signal based on singular spectral components whose energy percentage within the respiratory frequency band is greater than a preset respiratory energy ratio threshold;

[0095] The heartbeat signal is reconstructed based on the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold.

[0096] Furthermore, the reconstructing the respiratory signal based on the singular spectral component whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold comprises:

[0097] Determine the sum of the singular spectral components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold, and use the sum of the singular spectral components as a reconstructed respiratory signal;

[0098] Furthermore, the reconstructing the heartbeat signal based on the singular spectral component whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold comprises:

[0099] The sum of the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold is determined, and the sum of the singular spectral components is used as the reconstructed heartbeat signal.

[0100] Step 5: Perform Fourier transform on the reconstructed breathing and heartbeat signals respectively to obtain the life information of the human target.

[0101] In summary, the present application proposes a method for detecting life information based on a singular spectrum decomposition algorithm, including: using an ultra-wideband radar to detect an area to be detected, obtaining a radar echo matrix corresponding to the area to be detected, and filtering out background clutter in the radar echo matrix; extracting vital signs signals of human targets in the area to be detected according to the radar echo matrix after filtering out clutter; using a singular spectrum decomposition algorithm to decompose the vital signs signals to obtain the singular spectrum components corresponding to the vital signs signals; reconstructing the respiratory and heartbeat signals according to the energy percentage of each singular spectrum component in the respiratory and heartbeat frequency bands; Fourier transforming the reconstructed respiratory and heartbeat signals respectively to obtain the life information of the human target. The technical solution provided by the present invention, based on the singular spectrum decomposition algorithm, can accurately and quickly detect the life information of human targets.

[0102] Example 2

[0103] Figure 2 A life information detection system based on a singular spectrum decomposition algorithm is provided according to an embodiment of the present application, such as Figure 2 As shown, the system comprises:

[0104] The filtering module 100 is used to detect the area to be detected by using an ultra-wideband radar, obtain a radar echo matrix corresponding to the area to be detected, and filter out background clutter in the radar echo matrix;

[0105] An extraction module 200 is used to extract the vital sign signal of the human target in the detection area according to the radar echo matrix after filtering out the clutter;

[0106] A decomposition module 300, configured to decompose the vital sign signal using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal;

[0107] A reconstruction module 400, for reconstructing the respiratory and heartbeat signals according to the energy percentage of each singular spectral component in the respiratory and heartbeat frequency bands;

[0108] The transformation module 500 is used to perform Fourier transformation on the reconstructed breathing and heartbeat signals respectively to obtain the vital information of the human target.

[0109] In the embodiments of the present disclosure, Figure 3 As shown, the extraction module 200 includes:

[0110] A calculation unit 201 is used to calculate the sample entropy of the slow time series corresponding to each range gate in the radar echo matrix after filtering out clutter and the number of points occupied by the chest distance of the human target in the received pulse;

[0111] A first determining unit 202 is configured to use a range gate corresponding to a minimum sample entropy value as a range gate where the human target is located, and determine a location of the human target based on the range gate where the human target is located;

[0112] A second determining unit 203 is used to determine the adjacent range gate of the range gate corresponding to the position of the human target according to the number of points occupied by the position and the chest distance of the human target in the received pulse;

[0113] The extraction unit 204 is used to extract the echo signal of the range gate corresponding to the position of the human target and the echo signals of the adjacent range gates, and reorganize and arrange the extracted echo signals into rows to obtain the vital sign signal of the human target.

[0114] In the embodiment of the present disclosure, the decomposition module 300 is used to:

[0115] Decomposing the vital sign signal into the sum of m singular spectrum components and a trend term using a singular spectrum decomposition algorithm;

[0116] Wherein, the m is determined based on the number of iterations in the process of decomposing the vital sign signal.

[0117] In the embodiments of the present disclosure, Figure 4 As shown, the reconstruction module 400 includes:

[0118] The third determining unit 401 is used to respectively determine the energy percentage of each singular spectral component in the respiratory frequency band and the energy percentage of each singular spectral component in the heartbeat frequency band;

[0119] A screening unit 402 is used to screen out singular spectrum components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold and singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold in each singular spectrum;

[0120] A first reconstruction unit 403, configured to reconstruct a respiratory signal based on a singular spectral component whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold;

[0121] The second reconstruction unit 404 is configured to reconstruct the heartbeat signal based on the singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold.

[0122] Furthermore, the first reconstruction unit 403 is used to:

[0123] Determine the sum of the singular spectral components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold, and use the sum of the singular spectral components as a reconstructed respiratory signal;

[0124] The second reconstruction unit 404 is configured to:

[0125] The sum of the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold is determined, and the sum of the singular spectral components is used as the reconstructed heartbeat signal.

[0126] In summary, the present application proposes a life information detection system based on a singular spectrum decomposition algorithm, which can accurately and quickly detect the life information of a human target based on the singular spectrum decomposition algorithm.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0128] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0129] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for detecting life information based on a singular spectrum decomposition algorithm, characterized in that: The method comprises: Using an ultra-wideband radar to detect the area to be detected, obtaining a radar echo matrix corresponding to the area to be detected, and filtering out background clutter in the radar echo matrix; Extract the vital sign signals of human targets in the area to be detected according to the radar echo matrix after filtering out clutter; Decomposing the vital sign signal by using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal; Reconstruct the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands; The reconstructed breathing and heartbeat signals are respectively subjected to Fourier transformation to obtain the life information of the human target.

2. The method according to claim 1, characterized in that The method of extracting the vital sign signal of the human target in the area to be detected according to the radar echo matrix after filtering out the clutter comprises: Calculating the sample entropy of the slow time series corresponding to each range gate in the radar echo matrix after filtering out clutter and the number of points occupied by the chest distance of the human target in the received pulse; The range gate corresponding to the minimum value of the sample entropy is used as the range gate where the human target is located, and the position of the human target is determined based on the range gate where the human target is located; Determine the adjacent range gate of the range gate corresponding to the position of the human target according to the number of points occupied by the position and the chest distance of the human target in the received pulse; The echo signal of the range gate corresponding to the position of the human target and the echo signals of the adjacent range gates are extracted, and the extracted echo signals are reorganized and arranged in rows to obtain the vital sign signal of the human target.

3. The method according to claim 1, characterized in that The step of decomposing the vital sign signal by using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal includes: Decomposing the vital sign signal into the sum of m singular spectrum components and a trend term using a singular spectrum decomposition algorithm; Wherein, the m is determined based on the number of iterations in the process of decomposing the vital sign signal.

4. The method according to claim 1, characterized in that The reconstructing the breathing and heartbeat signals according to the energy percentage of each singular spectral component in the breathing and heartbeat frequency bands comprises: respectively determining the energy percentage of each singular spectral component in the respiratory frequency band and the energy percentage of each singular spectral component in the heartbeat frequency band; Selecting singular spectrum components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold and singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold in each singular spectrum; reconstructing a respiratory signal based on singular spectral components whose energy percentage within the respiratory frequency band is greater than a preset respiratory energy ratio threshold; The heartbeat signal is reconstructed based on the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold.

5. The method according to claim 4, characterized in that The reconstructing the respiratory signal based on the singular spectral component whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold comprises: Determine the sum of the singular spectral components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold, and use the sum of the singular spectral components as a reconstructed respiratory signal; The reconstructing the heartbeat signal based on the singular spectral component whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold comprises: The sum of the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold is determined, and the sum of the singular spectral components is used as the reconstructed heartbeat signal.

6. A life information detection system based on singular spectrum decomposition algorithm, characterized in that: include: A filtering module is used to detect the area to be detected using an ultra-wideband radar, obtain a radar echo matrix corresponding to the area to be detected, and filter out background clutter in the radar echo matrix; An extraction module is used to extract the vital sign signals of human targets in the area to be detected based on the radar echo matrix after clutter is filtered out; A decomposition module, used to decompose the vital sign signal using a singular spectrum decomposition algorithm to obtain singular spectrum components corresponding to the vital sign signal; A reconstruction module, used for reconstructing the breathing and heartbeat signals according to the energy percentage of each singular spectrum component in the breathing and heartbeat frequency bands; The transformation module is used to perform Fourier transformation on the reconstructed breathing and heartbeat signals respectively to obtain the life information of the human target.

7. The life information detection system as claimed in claim 6, characterized in that: The extraction module comprises: A calculation unit, used for calculating the sample entropy of the slow time series corresponding to each range gate in the radar echo matrix after filtering out clutter and the number of points occupied by the chest distance of the human target in the received pulse; A first determining unit is used to use the range gate corresponding to the minimum sample entropy value as the range gate where the human target is located, and determine the position of the human target based on the range gate where the human target is located; A second determining unit is used to determine the adjacent range gate of the range gate corresponding to the position of the human target according to the number of points occupied by the position and the chest distance of the human target in the received pulse; The extraction unit is used to extract the echo signal of the range gate corresponding to the position of the human target and the echo signal of the adjacent range gate, and reorganize and arrange the extracted echo signals into rows to obtain the vital sign signal of the human target.

8. The life information detection system as claimed in claim 6, characterized in that: The decomposition module is used to: Decomposing the vital sign signal into the sum of m singular spectrum components and a trend term using a singular spectrum decomposition algorithm; Wherein, the m is determined based on the number of iterations in the process of decomposing the vital sign signal.

9. The life information detection system according to claim 6, characterized in that: The reconstruction module comprises: A third determining unit is used to respectively determine the energy percentage of each singular spectral component in the respiratory frequency band and the energy percentage of each singular spectral component in the heartbeat frequency band; A screening unit, used to screen out singular spectrum components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold and singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold in each singular spectrum; A first reconstruction unit, configured to reconstruct a respiratory signal based on a singular spectral component whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold; The second reconstruction unit is used to reconstruct the heartbeat signal based on the singular spectrum components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold.

10. The life information detection system according to claim 9, characterized in that: The first reconstruction unit is used to: Determine the sum of the singular spectral components whose energy percentage in the respiratory frequency band is greater than a preset respiratory energy ratio threshold, and use the sum of the singular spectral components as a reconstructed respiratory signal; The second reconstruction unit is used for: The sum of the singular spectral components whose energy percentage in the heartbeat frequency band is greater than a preset heartbeat energy ratio threshold is determined, and the sum of the singular spectral components is used as the reconstructed heartbeat signal.

Images