CN108872977B - Life body double-station cooperative detection method based on single-channel ultra-wideband radar - Google Patents
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Abstract
The invention discloses a life body double-station cooperative detection method based on a single-channel ultra-wideband radar. The method can effectively detect a plurality of different strong and weak life body targets, can extract the distance information of each life body target relative to two radars at one time, can solve the position information of the life body target without pairing processing, can effectively reduce the false alarm and false alarm leakage probability under the condition of multiple targets, improves the accuracy and robustness of human body target detection, and can meet the requirement of detecting weak vital signs of a plurality of trapped persons under the occasions of disaster rescue and the like.
Description
Technical Field
The invention relates to a radar detection method, in particular to a life body double-station cooperative detection method based on a single-channel ultra-wideband radar.
Background
In the field of radar type life detection, in order to detect and locate a life target, the conventional method generally uses an ultra-wideband radar with a plurality of transceiving channels or an ultra-wideband radar with a plurality of single transceiving channels to simultaneously detect the life target. Aiming at the echo signal of the living body of a single transceiving channel, weak periodic breathing vital signs are extracted in a multi-pulse coherent accumulation mode, then distance information of the living body relative to a radar is obtained through target detection, and finally position information of the living body is calculated by using the distance information of the living body of a plurality of transceiving channels.
However, in the case of multiple animate objects, there are two main problems with this method of detecting and locating an animate object based on coherent accumulation. Firstly, due to scattering characteristics of a plurality of targets, difference of distances relative to a radar and multi-target shielding, energy difference of echo signals of different targets is large, target signals with tiny echo energy are not easy to detect, and even a missing detection phenomenon occurs due to annihilation of noise; secondly, the position information of the living bodies of a plurality of receiving and transmitting channels needs to be paired according to the target attribution, the pairing process is complicated, and false pairing is easily caused due to the false pairing problem.
Disclosure of Invention
The invention aims to provide a single-channel ultra-wideband radar-based living body double-station cooperative detection method for solving the problems, effectively detecting a plurality of different strong and weak living body targets and extracting each living body target at one time.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for detecting a living body in a two-station cooperative mode based on a single-channel ultra-wideband radar comprises the following steps:
(1) selecting a rectangular area, arranging two radars on one side of the rectangular area, wherein the two radars are marked as a first radar and a second radar respectively, the radars are frequency stepping radars, transmitters of the two radars face the rectangular area, the distance between the two radars is c, and the detection areas can be partially overlapped;
(2) the transmitters of the two radars simultaneously and continuously send out N periods of frequency stepping continuous wave signals, echo signals are generated after the frequency stepping continuous wave signals reach a life body target and are scattered, the echo signals are collected by the two radar receivers, and each radar obtains N periods of frequency stepping echo signals;
(3) carrying out IFFT (inverse fast Fourier transform) on frequency stepping echo signals received by a first radar and a second radar according to periods to form a fast and slow time data plane, and respectively storing the fast and slow time data plane into a matrix P with N rows and M columns1(N, M) and P2(N, M), wherein M is the number of IFFT points;
(4) and (4) respectively carrying out motion filtering on the two matrixes obtained in the step (3) according to slow time on the column by adopting a motion filter, and recording the matrix after the motion filtering as Tp1(N, M) and Tp2(N,M)；
(5) Low-pass filtering the two matrixes obtained in the step (4) by adopting a low-pass filter according to slow time of the column edge, and marking the filtered data matrix as TL1(N, M) and TL2(N,M)；
(6) Will TL1The (N, M) th (i ═ 1,2, …, M) column element and matrix TL2The j (j ═ 1,2, …, M) column element of (N, M) is used for making correlation coefficient calculation to form cross correlation coefficient matrix VM×M，VM×MThe formula for calculating the middle correlation coefficient is as follows:
wherein t isL1i(T) represents a matrix TL1(N, M) element of the t-th row, i-column, tL2j(T) represents a matrix TL2(N, M) elements in the (t) th row and j column, V (i, j) being the matrix VM×MRow i and column j;
(7) for cross correlation coefficient matrix VM×MConstant false alarm rate detection is carried out, and a matrix V is outputC(M,M)；
(8) Will matrix VCProcessing and merging a plurality of connected regions near each target in the (M, M) into single connected regions through an expansion algorithm, marking each single connected region as one target, and counting the number K of the targets;
(9) marking the two radars as a first radar and a second radar, and respectively extracting the center point coordinate (a) of each targetk，bk)，akDistance of target to radar number one, bkTo the eyesMarking the distance to radar number two, K (K is 1,2, …, K);
(10) establishing a coordinate system by taking the first radar as an origin and the extension line of the connection line of the two radars as an x axis, and calculating the actual position coordinate (x) of the target by using the following formulak,yk)；
Preferably, the method comprises the following steps: the frequency stepping radar is a single-channel frequency ultra-wideband stepping radar.
Preferably, the method comprises the following steps: motion filters can be divided into two categories, pulse cancellers and matched filters. For convenient calculation and programming, an average cancellation motion filter is adopted, and the specific filtering method in the step (4) is as follows: separately calculate P1And P2Average value of each row of elements, and subtracting the average value of each row of elements from all the elements in each row to eliminate direct current component in echo signal to obtain matrix Tp1(N, M) and Tp2(N,M)，Tp1(N, M) and Tp2The calculation formula for each element in (N, M) is as follows:
where N is 1,2, …, N, M is 1,2, …, M, which are the number of rows and columns, respectively, in the matrix.
Preferably, the method comprises the following steps: studies have shown that the human breathing frequency is between 0.2Hz and 0.7Hz, so the low pass filter is a finite impulse response (fir) low pass filter with a cut-off frequency of 1 Hz.
Preferably, the method comprises the following steps: in the step (7), the detection of the constant false alarm rate specifically comprises the following steps:
for cross correlation coefficient matrix VM×MDetecting the constant false alarm rate according to the set false alarm probability PfaCalculating the matrix VM×MThe decision threshold corresponding to each element V (i, j)
If it is
Setting V (i, j) to 1, otherwise setting V (i, j) to 0, and recording the result after judgment as VC(M,M)。
The overall thought of the invention is as follows: two-dimensional echo signal matrixes are formed by receiving echo signals in areas through two radars at different positions, the two matrixes are subjected to motion filtering, low-pass filtering and other operations respectively, then the two matrixes are subjected to cross-correlation calculation along a slow time dimension to form a cross-correlation coefficient matrix, and the specific position of a target is calculated and imaged by using the distance between the target and the two radars in the cross-correlation matrix. The method can effectively detect a plurality of different strong and weak life body targets, can extract the distance information of each life body target relative to two radars at one time, and can solve the position information of the life body target without pairing processing.
Compared with the prior art, the invention has the advantages that: the invention adopts different radars to carry out the cross-correlation method on the echo signals of the same target, detects and positions the human target by utilizing the correlation degree between the signals, namely the magnitude of the correlation coefficient, instead of detecting and positioning the human target by adopting the traditional method for comparing the magnitude of energy, can effectively reduce the false alarm and false alarm leakage probability caused by large target distance difference or large echo energy difference caused by shielding effect between the targets under the condition of multiple targets, improve the accuracy and the robustness of human target detection, and can meet the requirement of detecting weak vital signs of a plurality of trapped people under the occasions of disaster rescue and the like.
Drawings
FIG. 1 is a diagram: a schematic diagram of a radar detection area;
FIG. 2 is a diagram of: an embodiment radar and target location schematic;
FIG. 3 is a diagram of: a fast and slow time data matrix received by the first radar;
FIG. 4 is a diagram of: a fast and slow time data matrix received by the second radar;
FIG. 5 is a diagram: a first radar is subjected to motion filtering to obtain a speed time data matrix;
FIG. 6 is a diagram of: a second radar is subjected to motion filtering to obtain a fast and slow time data matrix;
FIG. 7 is a diagram of: the first radar is subjected to low-pass filtering to obtain a fast and slow time data matrix;
FIG. 8 is a diagram of: the second radar is subjected to low-pass filtering to obtain a fast and slow time data matrix;
FIG. 9 is a diagram of: a cross correlation coefficient matrix of two radar echo signals;
FIG. 10 is a diagram: the cross correlation coefficient matrix is subjected to a constant false alarm rate detection result;
FIG. 11 is a diagram of: and finally, positioning an imaging graph by the target.
Detailed Description
The invention will be further explained with reference to the drawings.
Example 1: referring to fig. 1, a method for detecting a living body in a two-station cooperative manner based on a single-channel ultra-wideband radar includes the following steps:
(1) selecting a rectangular area, arranging two radars on one side of the rectangular area, wherein the two radars are marked as a first radar and a second radar respectively, the radars are frequency stepping radars, transmitters of the two radars face the rectangular area, the distance between the two radars is c, and the detection areas can be partially overlapped;
(2) the transmitters of the two radars simultaneously and continuously send out N periods of frequency stepping continuous wave signals, echo signals are generated after the frequency stepping continuous wave signals reach a life body target and are scattered, the echo signals are collected by the two radar receivers, and each radar obtains N periods of frequency stepping echo signals;
(3) carrying out IFFT (inverse fast Fourier transform) on frequency stepping echo signals received by a first radar and a second radar according to periods to form a fast and slow time data plane, and respectively storing the fast and slow time data plane into a matrix P with N rows and M columns1(N, M) and P2(N, M), wherein M is the number of IFFT points;
(4) and (4) respectively carrying out motion filtering on the two matrixes obtained in the step (3) according to slow time on the column by adopting a motion filter, and recording the matrix after the motion filtering as Tp1(N, M) and Tp2(N,M)；
(5) Low-pass filtering the two matrixes obtained in the step (4) by adopting a low-pass filter according to slow time of the column edge, and marking the filtered data matrix as TL1(N, M) and TL2(N,M)；
(6) Will TL1The (N, M) th (i ═ 1,2, …, M) column element and matrix TL2The j (j ═ 1,2, …, M) column element of (N, M) is used for making correlation coefficient calculation to form cross correlation coefficient matrix VM×M，VM×MThe formula for calculating the middle correlation coefficient is as follows:
wherein t isL1i(T) represents a matrix TL1(N, M) element of the t-th row, i-column, tL2j(T) represents a matrix TL2(N, M) elements in the (t) th row and j column, V (i, j) being the matrix VM×MRow i and column j;
(7) for cross correlation coefficient matrix VM×MConstant false alarm rate detection is carried out, and a matrix V is outputC(M,M)；
(8) Will matrix VCProcessing and merging a plurality of connected regions near each target in the (M, M) into single connected regions through an expansion algorithm, marking each single connected region as one target, and counting the number K of the targets;
(9) marking the two radars as a first radar and a second radar, and respectively extracting the center point coordinate (a) of each targetk，bk)，akDistance of target to radar number one, bkK (K ═ 1,2, …, K) is the distance of the target to radar number two;
(10) establishing a coordinate system by taking the first radar as an origin and the extension line of the connection line of the two radars as an x axis, and calculating the actual position coordinate (x) of the target by using the following formulak,yk)；
In this embodiment: the frequency stepping radar is a single-channel frequency ultra-wideband stepping radar;
the motion filter is an average cancellation motion filter, and the specific filtering method in the step (4) is as follows: separately calculate P1And P2Average value of each column element in the series, and let eachSubtracting the average value of each row of elements from all the elements in the row to eliminate the DC component in the echo signal to obtain the matrix Tp1(N, M) and Tp2(N,M)，Tp1(N, M) and Tp2The calculation formula for each element in (N, M) is as follows:
where N is 1,2, …, N, M is 1,2, …, M, which are the number of rows and columns, respectively, in the matrix.
The low pass filter is a finite impulse response (fir) low pass filter with a cut-off frequency of 1 Hz.
In the step (7), the detection of the constant false alarm rate specifically comprises the following steps:
for cross correlation coefficient matrix VM×MDetecting the constant false alarm rate according to the set false alarm probability PfaCalculating the matrix VM×MThe decision threshold corresponding to each element V (i, j)
If it is
Setting V (i, j) to 1, otherwise setting V (i, j) to 0, and recording the result after judgment as VC(M,M)。
Example 2: see fig. 1 to 11;
(1) two radars are respectively arranged at coordinates (0,0) and (1,0) in MATLAB simulation software; the first radar position is (0,0), and the second radar position is (1, 0); in order to verify the accuracy of the method, a moving target and a static target are arranged in a coordinate system, wherein the moving target is respectively positioned at coordinates (1,6) and (2,4), sinusoidal vibration is carried out at the frequency of 0.2Hz and the amplitude of 0.01m, and the power of a target echo signal positioned at the coordinate (1,6) is 0.3 times that of the target echo signal positioned at the coordinate (2, 4); the stationary target simulates stationary background clutter at coordinates (0, 2). The central frequency of the radar is set to be 2GHz, the bandwidth is set to be 1.5GHz, Gaussian white noise is added, and the signal-to-noise ratio is 10 dB. The positions of the target and the radar are shown in fig. 2, and the detection distance of the radar adopted in the embodiment is 9 meters;
(2) the two radar transmitters simultaneously and continuously send 3840 periods of frequency stepping continuous wave signals, target echo signals after a target of a living body is scattered are collected by the two radar receivers, and each radar obtains an ultra-wideband frequency stepping echo signal with N being 3840 periods;
(3) respectively carrying out inverse Fourier transform (IFFT) pulse compression on ultra-wideband frequency step echo signals with N-3840 periods obtained by each radar cycle by cycle to form a fast-slow time data plane, and storing the fast-slow time data plane into two fast-slow time data matrixes P1(3840,400) and P2(3840,400), as shown in fig. 3, fig. 4; wherein M is 400 points of IFFT;
3 vertical lines are clearly seen in fig. 3 and 4, and a static target, a target 1 and a target 2 are sequentially arranged from left to right;
(4) respectively aiming at two fast and slow time data matrixes P1(N, M) and P2(N, M) according to the slow time of the column edge, average cancellation processing is carried out to filter out static background clutter, namely, the average value of all elements of each column is subtracted from each element in each column, and data after average cancellation are respectively stored in a matrix Tp1(3840,400) and Tp2(3840,400), as shown in FIGS. 5 and 6. And it can be seen that the signal scattered back by the static clutter target is filtered out and cannot be seen from the matrix. In fig. 5 and 6, 2 vertical lines are clearly seen, namely a target 1 and a target 2 from left to right;
(5): constructing a fir low-pass filter with 50 orders and cut-off frequency of 1Hz by using MATLAB software, and respectively aligning the data matrix Tp1(3840,400) and Tp2(3840,400) low-pass filtering by the slow time of the column edge to suppress the high-frequency clutter and noise, and recording the filtered data matrix as TL1(3840,400) and TL2(3840,400), as shown in fig. 7, fig. 8; in fig. 7 and 8, 2 vertical lines are clearly seen, namely, an object 1 and an object 2 from left to right;
(6) will TL1(3840,400) th (i ═ 1,2, …,400) column element and matrix TL2(3840,400) performing correlation coefficient calculation on j (j ═ 1,2, …,400) th column elements, and storing the calculation result in a cross correlation coefficient matrix V400×400Middle and momentThe calculation formula of the relational number in the array is as follows:
wherein t isL1i(T) represents a matrix TL1(3840,400) element of t row i column, tL2j(T) represents a matrix TL2(3840,400) row and column elements, V (i, j) being matrix VM×MIth row and j column elements, cross correlation coefficient matrix V400×400The area with larger middle elements is the area where the target is located, as shown in fig. 9. It can be seen that there are two regions of high correlation, i.e. 2 vibration targets, matrix V400×400The middle element position may represent the range of the target to both radars. In FIG. 9, the shadow of 2 clusters can be clearly seen, and the object 1 and the object 2 are sequentially arranged from left to right;
(7) according to the set false alarm probability PfaCalculate matrix V0.01 ═ 0.01400×400The decision threshold corresponding to each element V (i, j)
If it is
Setting V (i, j) to 1, otherwise setting V (i, j) to 0, and recording the result after judgment as VC(400 ), as shown in FIG. 10, 2 black dots are clearly seen in FIG. 10, and the target 1 and the target 2 are sequentially arranged from left to right;
(8) the matrix V obtained in the step seven is processed by an stencil function in MATLABC(400 ) the multiple connected regions near each target are merged into a single connected region by the expansion algorithm,
(9) extracting the coordinates of the center point of each region as the distance coordinates (a) between the target and the radar by using a regionprops function in MATLAB1＝6.01,b1＝6.02)，(a2＝4.49,b24.14) in which a1Denotes the distance between target 1 and radar No. 1, b1Denotes the distance between object No. 1 and Radar No. 2, a2Denotes the distance between object 2 and radar number 1, b2Representing the distance between the target 2 and the radar No. 2;
(10) according to the obtained distance (a) between the target and two radars1,b1) And (a)2,b2) The actual position coordinates (1.16m,6.00m), (1.98m,4.02m) of the target were calculated by the triangulation positioning algorithm of fig. 1, and the error set at the beginning of the example was small, and MATLAB imaging is shown in fig. 11. In fig. 11, 2 circles are clearly visible, which are object 1 and object 2 from left to right.
In summary, the invention performs cross-correlation operation on the echo signals received by two different position radars in a matrix processing way and along a slow time, realizes stable detection and positioning of a living body target, can effectively reduce the probability of false alarm and false alarm caused by distance, position and the like, and can meet the requirements on detection and positioning of the living body in different scenes.
Claims (5)
1. A method for the double-station cooperative detection of a life body based on a single-channel ultra-wideband radar is characterized in that: the method comprises the following steps:
(1) selecting a rectangular area, arranging two radars on one side of the rectangular area, wherein the two radars are marked as a first radar and a second radar respectively, the radars are frequency stepping radars, transmitters of the two radars face the rectangular area, the distance between the two radars is c, and the detection areas can be partially overlapped;
(2) the transmitters of the two radars simultaneously and continuously send out N periods of frequency stepping continuous wave signals, echo signals are generated after the frequency stepping continuous wave signals reach a life body target and are scattered, the echo signals are collected by the two radar receivers, and each radar obtains N periods of frequency stepping echo signals;
(3) carrying out IFFT (inverse fast Fourier transform) on frequency stepping echo signals received by a first radar and a second radar according to periods to form a fast and slow time data plane, and respectively storing the fast and slow time data plane into a matrix P with N rows and M columns1(N, M) and P2(N, M), wherein M is the number of IFFT points;
(4) and (4) respectively carrying out motion filtering on the two matrixes obtained in the step (3) according to slow time on the column by adopting a motion filter, and recording the matrix after the motion filtering as Tp1(N, M) and Tp2(N,M)；
(5) Low-pass filtering the two matrixes obtained in the step (4) by adopting a low-pass filter according to slow time of the column edge, and marking the filtered data matrix as TL1(N, M) and TL2(N,M)；
(6) Will TL1The ith row of elements in (N, M) and matrix TL2Performing correlation coefficient calculation on the jth column element of the (N, M) to form a cross correlation coefficient matrix VM×M，VM×MThe formula for calculating the middle correlation coefficient is as follows:
wherein i is 1,2, …, M, j is 1,2, …, M, tL1i(T) represents a matrix TL1(N, M) element of the t-th row, i-column, tL2j(T) represents a matrix TL2(N, M) elements in the (t) th row and j column, V (i, j) being the matrix VM×MThe ith row and the jth column of elements,
representation matrix TL1(N, M) the average of N elements in the ith column,
representation matrix TL2(N, M) average of N elements in jth column;
(7) for cross correlation coefficient matrix VM×MConstant false alarm rate detection is carried out, and a matrix V is outputC(M,M)；
(8) Will matrix VCProcessing and merging a plurality of connected regions near each target in the (M, M) into single connected regions through an expansion algorithm, marking each single connected region as one target, and counting the number K of the targets;
(9) marking the two radars as a first radar and a second radar, and respectively extracting the center point coordinate (a) of each targetk，bk)，akDistance of target to radar number one, bkThe distance between the target and the second radar is K, which is 1,2, … and K;
(10) using a radar as an originEstablishing a coordinate system by using the extension line of the connection line of the two radars as an x axis, and calculating the actual position coordinate (x) of the target by using the following formulak,yk)；
2. The method for the two-station cooperative detection of the life body based on the single-channel ultra-wideband radar as claimed in claim 1, wherein: the frequency stepping radar is a single-channel frequency ultra-wideband stepping radar.
3. The method for the two-station cooperative detection of the life body based on the single-channel ultra-wideband radar as claimed in claim 1, wherein: the motion filter is an average cancellation motion filter, and the specific filtering method in the step (4) is as follows: separately calculate P1(N, M) and P2Average value of each row of elements in (N, M), and subtracting the average value of each row of elements from all the elements in each row to eliminate direct current component in echo signal to obtain matrix Tp1(N, M) and Tp2(N,M)，Tp1(N, M) and Tp2The calculation formula for each element in (N, M) is as follows:
where N is 1,2, …, N, M is 1,2, …, M, which are the number of rows and columns, respectively, in the matrix.
4. The method for the two-station cooperative detection of the life body based on the single-channel ultra-wideband radar as claimed in claim 1, wherein: the low pass filter is a finite impulse response (fir) low pass filter with a cut-off frequency of 1 Hz.
5. The method for the two-station cooperative detection of the life body based on the single-channel ultra-wideband radar as claimed in claim 1, wherein: in step (7), the constant false alarm rateThe detection is specifically as follows: for cross correlation coefficient matrix VM×MDetecting the constant false alarm rate according to the set false alarm probability PfaCalculating the matrix VM×MThe decision threshold corresponding to each element V (i, j)
If it is
Setting V (i, j) to 1, otherwise setting V (i, j) to 0, and recording the result after judgment as VC(M,M)。
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