CN112162269B - Sea clutter suppression and target detection method based on singular spectrum decomposition - Google Patents
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
A sea clutter suppression and target detection method based on singular spectrum decomposition (Singular Spectrum Analysis, SSA) includes the steps of firstly calculating an autocorrelation function of echo data, constructing a Toeplitz matrix of the echo data by taking a first zero crossing point position of the autocorrelation function as a dimension of an echo data track matrix of a reconstruction radar, obtaining the number of principal components (Principal Component Analysis, PCA), and judging whether an echo contains a target according to the number of PCA; then carrying out singular spectrum decomposition on the radar echo, reconstructing time sequences corresponding to the main component and the secondary component (Minor Component Analysis, MCA), and when the echo contains a target, taking the time sequence corresponding to the maximum main component and discarding the time sequences corresponding to other components; otherwise, the time sequence corresponding to the smallest secondary component is selected and the time sequences corresponding to other components are omitted, and the processing process can be regarded as self-adaptive filtering independent of an echo statistical model, so that sea clutter intensity in processed radar echo data is effectively suppressed, and the detection probability of a subsequent weak and small target is improved.
Description
Technical Field
The invention belongs to the technical field of target detection, relates to target detection under a sea clutter background, and discloses a sea clutter suppression and target detection method based on singular spectrum decomposition.
Background
Sea clutter refers to echoes generated when sea observation radar beams are irradiated to sea surfaces, characteristics of the sea clutter are closely related to factors such as area of a region covered by the radar beams, sea wave height (i.e. sea conditions), main frequency of radar operation, bandwidth and the like, generally the sea clutter reflects motion characteristics of wave discharge caused by gravity and micro motion characteristics of tubular fine waves caused by sea surface tension, namely, the sea clutter comprises 2 main components, the amplitude, bandwidth and other parameters of the sea clutter show non-stable and nonlinear characteristics along with the sea conditions, as shown in (a) and (b) in the attached figure 1, a time-domain amplitude chart of sea clutter data (http:// sigma. Ece. McMaster. Ca/IPIX/datapath. Html) measured by using the sea clutter radar of the university of Canada exists, and the intensity and quantity of peaks increase along with the increase of sea conditions, and figure 2 is a frequency spectrogram of the sea clutter data, and the bandwidth spectrum chart is remarkably widened due to the micro motion characteristics caused by the sea surface tension. The research on the weak and small target detection method under the sea clutter background is always a hot spot in the radar signal processing field, is used for detecting sea surface floaters (floating ice, small ships, aircraft debris and the like) in the civil field, and provides technical support for navigation of civil ships, maritime search and rescue and the like. In the military field, the method is used for detecting ships, submarine periscopes, aircrafts flying close to the sea surface and the like with stealth characteristics, the traditional method is to consider sea clutter as a complex stable random process, a statistically significant probability model such as Weibull distribution, log-normal distribution, a composite K distribution model, pareto distribution and the like is established through a large amount of observation data, and then the target detection is realized by using a mature detection method. However, the assumption of sea clutter stationarity is based on short observation time, in order to increase the detection probability of weak and small targets, the radar has to increase the observation time of the target area to increase the target echo energy after coherent accumulation, but the sea clutter will not be a smooth random process with the increase of the observation time, as shown in (a) and (b) in fig. 3, the Pareto distribution parameter of the sea clutter dataset of CSIR TFC15_023.01 in south africa, and the shape parameter k and the scale parameter sigma of the parameter are randomly fluctuated according to different distance units, which inevitably causes larger measurement errors if fixed model parameters are adopted.
In summary, for a sea observation radar to detect a weak and small target on the sea, the echo energy of the target is usually enhanced by increasing the observation time of a sea surface target area, however, the intensity of sea echo (sea clutter) received in a large coherence processing time is correspondingly enhanced, and the sea clutter presents nonlinear, non-gaussian and nonstationary characteristics, the conventional weak and small target detection method under the sea clutter background regards the sea clutter as obeying a probability density distribution, the target is detected by the difference between probability density distribution characteristics when the target exists or not, the conventional method ignores the nonstationary characteristics of the sea clutter, and the false alarm probability is too high in high sea conditions and is not suitable for target detection in the large coherence processing time.
In order to solve the above problems, a great deal of research work has been carried out by expert students in the field of radar engineering, and a certain number of research results have been achieved. One of the classical solutions is to dynamically track the statistical characteristics of radar echoes, segment the radar echoes according to the principle of similar characteristics, and solve part of problems on the engineering level, but as the sea conditions are improved, the segmentation of radar echo data is shorter and shorter, and finally, the radar echo data has no essential difference from the short-time observation situation, so that the detection of weak and small targets still has higher false alarm probability and lower discovery probability, therefore, the signal processing method based on the statistical characteristics does not meet the engineering requirements under the high sea conditions, and a sea clutter suppression method independent of statistical models is required to be searched, thereby solving the problem.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a sea clutter suppression and target detection method based on singular spectrum decomposition, which comprises the steps of firstly calculating an autocorrelation function of echo data, taking a first zero crossing point of the autocorrelation function as dimensions of a Toeplitz matrix and a track matrix of reconstructed radar echo data, further obtaining the number of principal components (Principal Component, PCA), and judging whether an echo contains a target according to the number of PCA; and then, carrying out self-adaptive filtering on the radar echo by utilizing singular spectrum decomposition, and carrying out Constant False Alarm (CFAR) target detection on the processed radar echo.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a sea clutter suppression and target detection method based on singular spectrum decomposition comprises the following steps:
s1) for coherent radar echoes, S2) for incoherent radar echoes, S3) is performed sequentially;
s2) obtaining absolute values of a radar echo I, Q data sequence;
s3) calculating an autocorrelation function value of echo data;
s4) calculating a first zero crossing point position of an echo data autocorrelation function, and estimating the correlation time of echo data, wherein the first zero crossing point position is used as a window length L for constructing the dimension of an echo data Toeplitz matrix C and the subsequent singular spectrum decomposition;
s5) converting radar echo data into a Toeplitz matrix C according to a window length L;
s6) decomposing the eigenvalue of the Toeplitz matrix C to extract the eigenvalue sigma i And corresponding feature vector v i I=1, 2, … L, singular spectrum is calculatedSequentially accumulating R i R corresponds to when the accumulated value is greater than or equal to 0.9 i Number N of (2) p As principal component sigma ip Is the number of (3); definition sigma ip Is the main component of radar echo data, v ip For its eigenvector, ip=1, 2, …, N p The method comprises the steps of carrying out a first treatment on the surface of the Definition sigma jm V is the minor component of radar echo data jm Jm=1, 2, …, L-N for its eigenvector p ；
In the principal component sigma ip Minor component sigma jm After the determination, calculating:
s6.1 signal to noise ratio SCR (signal to clutter ratio)
S6.2 signal to noise ratio SNR (signal to noise ratio)
S6.3 if the echo contains only sea clutter, the clutter ratio CNR (clutter to noise ratio) is calculated
S7) performing singular spectrum decomposition on the radar echo, wherein the singular spectrum decomposition comprises the following steps:
s7.1: rearranging the feature vectors extracted in S6) from large to small according to the corresponding feature values to V= (V) ip ,v jm ) The method comprises the steps of carrying out a first treatment on the surface of the After the principal component (Principal Component Analysis, PCA) and the secondary component (Minor Component Analysis, MCA) are distinguished according to the accumulation result of the singular spectrum, the sequences are respectively arranged from big to small, and the operation is to correspondingly adjust the sequence of the feature vectors, namely the feature vectors are arranged according to the sequence of ip and jm so as to correspond to the positions of the PCA and the MCA;
s7.2. echo of radar x= { X k The method comprises the steps of (1) converting the data into a 2-dimensional track matrix H according to a delay coordinate phase space reconstruction (delay coordinate phase space reconstruction), wherein k=1, 2, … N and N are radar echo data lengths; a corresponding embedding dimension (embedding dimension) m=l, a delay time (time delay) τ=1;
where the number of rows m=n- (M-1) τ of matrix H.
S7.3, calculating the projection W=HV of the 2-dimensional track matrix H on the feature space formed by the feature vectors, and decomposing W into N corresponding to the main components according to the feature vectors corresponding to the main components and the secondary components p Sub-matrix W ip Corresponding to the secondary component (L-N p ) Sub-matrix W jm Two sets of matrices;
s7.4 for N respectively p Sub-matrix W ip And (L-N) p ) Sub-matrix W jm Performing an inverse diagonal element averaging process (inverse Hankelization process) to convert it into a one-dimensional time series X p ＝{x ip And X m ＝{x jm Obtaining time sequences corresponding to the primary component and the secondary component;
s8) if the number of principal components of the radar echo N p >E t Preserving the maximum principal component sigma ip Corresponding time series x ip At this time, ip=1, discarding the time series corresponding to other components; if N p ≤E t Then reserve X m ＝{x jm The last time sequence in the sequence, i.e. the time corresponding to the smallest secondary componentThe inter-sequence, discard the time sequence corresponding to other components; wherein E is t Judging whether the radar echo contains a threshold of a target;
preferably, said E t For determining whether the radar echo contains the threshold of the target, i.e. the number of principal components contained in the sea clutter is not greater than 2, the 2 principal components reflect the intensity of gravitational wave and sea surface tension wave of the sea wave, respectively.
S9) sequentially carrying out S2) to S8) on radar echoes received in the coherent processing time, arranging the reserved time sequence into a 2-dimensional echo matrix according to the arrival time of echo pulses, and then carrying out target detection by using a constant false alarm detection (CFAR) method; the whole radar echo processing procedure is defined as SSA-CFAR detection.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a sea clutter suppression method which does not depend on a sea clutter statistical model, and can detect weak and small targets which cannot be found by the traditional method under the given sea clutter background, thereby obviously improving the detection precision.
Drawings
Fig. 1 shows the IPIX radar actual sea clutter, where (a) is the low sea state sea clutter and (b) is the high sea state sea clutter.
Fig. 2 shows the actual sea clutter spectrum of the IPIX radar, where (a) is the low sea state sea clutter spectrum and (b) is the high sea state sea clutter spectrum.
Fig. 3 shows Pareto distribution parameters of the south africa CSIR TFC 15-023.01 sea clutter data set, where (a) is the shape parameter k and (b) is the scale parameter sigma.
Fig. 4 is a flow chart of the present invention.
Fig. 5 shows the target ship echo in the SSA-processed range bin 11.
FIG. 6 shows sea clutter in SSA-processed range bin 11, where (a) is the principal component σ 2 Corresponding time series, (b) is sigma 3 A corresponding time series.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.
The invention relates to a sea clutter suppression and target detection method based on singular spectrum decomposition (Singular Spectrum Analysis, SSA), which comprises the steps of firstly calculating an autocorrelation function of echo data, constructing a Toeplitz matrix of the echo data by taking a first zero crossing point position of the autocorrelation function as a dimension of a reconstructed radar echo data track matrix, obtaining the number of principal components (Principal Component Analysis, PCA), and judging whether an echo contains a target according to the number of PCA; then carrying out singular spectrum decomposition on the echo track matrix, reconstructing time sequences corresponding to the main component and the secondary component (Minor Component Analysis, MCA), and when the echo contains a target, taking the time sequence corresponding to the maximum main component and discarding the time sequences corresponding to other components; otherwise, the time sequence corresponding to the smallest secondary component is selected and the time sequences corresponding to other components are omitted, and the processing process can be regarded as self-adaptive filtering independent of an echo statistical model, so that sea clutter intensity in processed radar echo data is effectively suppressed, and the detection probability of a subsequent weak and small target is improved.
The specific steps of the invention are shown in fig. 4, comprising:
s1) for coherent radar returns { y } i ＝v i +ju i Sequentially executing S2), for incoherent radar returns { x } i Turning to S3), i=1, 2, … N, v i 、u i The real part I sequence and the imaginary part Q sequence of the radar echo data are respectively;
s2) obtaining absolute values of a radar echo I, Q data sequence;
s3) calculating an autocorrelation function value of the echo data:
s4) calculating a first zero crossing point position of an echo data autocorrelation function as a window length L for constructing the dimension of an echo data Toeplitz matrix C and subsequent singular spectrum decomposition;
s5) converting radar echo data into a Toeplitz matrix C according to a window length L;
s6) performing eigenvalue decomposition on Toeplitz matrix C, namely [ V, D]=eig (C) (Matlab function), extracting eigenvalue σ from diagonal of D i And corresponding feature vector v i I=1, 2, … L, singular spectrum is calculatedSequentially accumulating R i R corresponds to when the accumulated value is greater than or equal to 0.9 i Number N of (2) p As principal component sigma ip Is the number of (3); definition sigma ip Is the main component of radar echo data, v ip For its eigenvector, ip=1, 2, …, N p The method comprises the steps of carrying out a first treatment on the surface of the Definition sigma jm V is the minor component of radar echo data jm Jm=1, 2, …, L-N for its eigenvector p The method comprises the steps of carrying out a first treatment on the surface of the Let E according to the conclusion obtained by the principal component analysis of the sea clutter t And the number of principal components contained in the sea clutter is not more than 2, and the principal components of the 2 principal components respectively reflect the gravity wave and the sea surface tension wave intensity of the sea wave. In the principal component sigma ip Minor component sigma jm After the determination, it can be calculated:
s6.1 signal to noise ratio SCR (signal to clutter ratio)
S6.2 signal to noise ratio SNR (signal to noise ratio)
S6.3 if the echo contains only sea clutter, the clutter ratio CNR (clutter to noise ratio) is calculated
S7) performing singular spectrum decomposition on the radar echo, wherein the singular spectrum decomposition comprises the following steps:
s7.1: rearranging the feature vectors extracted in S6) from large to small according to the corresponding feature values to V= (V) ip ,v jm ) The method comprises the steps of carrying out a first treatment on the surface of the After the principal component (Principal Component Analysis, PCA) and the secondary component (Minor Component Analysis, MCA) are distinguished according to the accumulation result of the singular spectrum, the sequences are respectively arranged from big to small, and the operation is to correspondingly adjust the sequence of the feature vectors, namely the feature vectors are arranged according to the sequence of ip and jm so as to correspond to the positions of the PCA and the MCA;
s7.2. echo of radar x= { X k The method comprises the steps of (1) converting the data into a 2-dimensional track matrix H according to a delay coordinate phase space reconstruction (delay coordinate phase space reconstruction), wherein k=1, 2, … N and N are radar echo data lengths; a corresponding embedding dimension (embedding dimension) m=l, a delay time (time delay) τ=1;
where M represents the number of rows of matrix H, m=n- (M-1) τ.
S7.3, calculating the projection W=HV of the 2-dimensional track matrix H on the feature space formed by the feature vectors, and decomposing W into N corresponding to the main components according to the feature vectors corresponding to the main components and the secondary components p Sub-matrix W ip Corresponding to the secondary component (L-N p ) Sub-matrix W jm Two sets of matrices;
s7.4 for N respectively p Sub-matrix W ip And (L-N) p ) Sub-matrix W jm Performing an inverse diagonal element averaging process (inverse Hankelization process) to convert it into a one-dimensional time series X p ＝{x ip And X m ＝{x jm Obtaining time sequences corresponding to the primary component and the secondary component;
s8) if the number of principal components of the radar echo N p >E t Preserving the maximum principal component sigma ip Corresponding time series x ip At this time, ip=1, discarding the time series corresponding to other components; if N p ≤E t Then reserve X m ＝{x jm Discarding the last time sequence of the components, namely the time sequence corresponding to the smallest secondary component; wherein E is t Judging whether the radar echo contains a threshold of a target;
as shown in fig. 5, the target ship echo in the south africa CSIR TFC 15-023.01 sea clutter data set distance unit 11 after SSA processing; the sea clutter separated for this range bin is shown in FIG. 6, where (a) is the principal component σ 2 Corresponding time series, (b) is sigma 3 A corresponding time series.
S9) sequentially carrying out S2) to S8) on radar echoes received in the coherent processing time, arranging the reserved time sequence into a 2-dimensional echo matrix according to the arrival time of echo pulses, and then carrying out target detection by using a constant false alarm detection (CFAR) method; the whole radar echo processing procedure is defined as SSA-CFAR detection.
The south Africa CSIR TFC15_023.01 sea clutter data set is taken as a processing object, the data set comprises echo I-Q data of 31 distance units, the data length is 33001, the distance unit 11 comprises target ship echoes, the distance units 9-10 and 12-13 are interference areas formed by the target ship and the sea surface, after S2) to S8) processing, CA-CFAR and OS-CFAR are respectively adopted to detect targets, the number of training distance units is 16, the number of protection distance units is 2, and the target false alarm probability is P fa_goal ＝10 -3 Target detection scanning distance units 9-13, and CA-CFAR discovery probability P d = 0.9876, false alarm probability P fa ＝3.0302×10 -5 The method comprises the steps of carrying out a first treatment on the surface of the Discovery probability P of OS-CFAR d = 0.9907, false alarm probability P fa ＝1.2121×10 -4 。
To verify the advancement of the present invention, the target detection is directly performed on the data set, and the discovery probability P of CA-CFAR d = 0.1857, false alarm probability P fa ＝6.0604×10 -5 The method comprises the steps of carrying out a first treatment on the surface of the Discovery probability P of OS-CFAR d = 0.3268, false alarm probability P fa ＝1.2121×10 -4 。
Claims (5)
1. A sea clutter suppression and target detection method based on singular spectrum decomposition is characterized by comprising the following steps:
s1) for coherent radar echoes, S2) for incoherent radar echoes, S3) is performed sequentially;
s2) obtaining absolute values of a radar echo I, Q data sequence;
s3) calculating an autocorrelation function value of echo data;
s4) calculating a first zero crossing point position of an echo data autocorrelation function as a window length L for constructing the dimension of an echo data Toeplitz matrix C and subsequent singular spectrum decomposition;
s5) converting radar echo data into a Toeplitz matrix C according to a window length L;
s6) decomposing the eigenvalue of the Toeplitz matrix C to extract the eigenvalue sigma i And corresponding feature vector v i I=1, 2, … L, singular spectrum is calculatedSequentially accumulating R i R corresponds to when the accumulated value is greater than or equal to 0.9 i Number N of (2) p As principal component sigma ip Is the number of (3); definition sigma ip Is the main component of radar echo data, v ip For its eigenvector, ip=1, 2, …, N p The method comprises the steps of carrying out a first treatment on the surface of the Definition sigma jm V is the minor component of radar echo data jm Jm=1, 2, …, L-N for its eigenvector p ；
S7) performing singular spectrum decomposition on the radar echo, wherein the singular spectrum decomposition comprises the following steps:
s7.1: rearranging the feature vectors extracted in S6) from large to small according to the corresponding feature values to V= (V) ip ,v jm )；
S7.2. echo of radar x= { X k The method comprises the steps of converting the data into a 2-dimensional track matrix H according to a delay coordinate phase space reconstruction method, wherein k=1, 2, … N and N are radar echo data lengths; corresponding embedded dimension m=l, delay time τ=1;
s7.3, calculating the projection W=HV of the 2-dimensional track matrix H on the feature space formed by the feature vectors, and decomposing W into N corresponding to the main components according to the feature vectors corresponding to the main components and the secondary components p Sub-momentArray W ip Corresponding to the secondary component (L-N p ) Sub-matrix W jm Two sets of matrices;
s7.4 for N respectively p Sub-matrix W ip And (L-N) p ) Sub-matrix W jm Performing inverse diagonal element averaging to convert it into one-dimensional time series X p ＝{x ip And X m ＝{x jm Obtaining time sequences corresponding to the primary component and the secondary component;
s8) if the number of principal components of the radar echo N p >E t Preserving the maximum principal component sigma ip Corresponding time series x ip At this time, ip=1, discarding the time series corresponding to other components; if N p ≤E t Then reserve X m ＝{x jm Discarding the last time sequence of the components, namely the time sequence corresponding to the smallest secondary component; wherein E is t Judging whether the radar echo contains a threshold of a target;
s9) sequentially carrying out S2) to S8) on radar echoes received in the coherent processing time, arranging the reserved time sequence into a 2-dimensional echo matrix according to the arrival time of echo pulses, and then carrying out target detection by using a constant false alarm detection method; the whole radar echo processing procedure is defined as SSA-CFAR detection.
2. The method for suppressing sea clutter and detecting targets based on singular spectrum decomposition according to claim 1, wherein in S4), the window length L of the singular spectrum decomposition is determined by calculating the first zero crossing point of the autocorrelation function of the echo data and estimating the correlation time of the echo data.
3. The method for sea clutter suppression and target detection based on singular spectrum decomposition according to claim 1, wherein in S6), the principal component σ is ip Minor component sigma jm After the determination, calculating:
s6.1 signal to noise ratio SCR
S6.2 SNR
S6.3 if the echo only contains sea clutter, calculating the clutter noise ratio CNR
4. The method for sea clutter suppression and target detection based on singular spectrum decomposition according to claim 1, wherein the E is t For determining whether the radar echo contains the threshold of the target, i.e. the number of principal components contained in the sea clutter is not greater than 2, the 2 principal components reflect the intensity of gravitational wave and sea surface tension wave of the sea wave, respectively.
5. The method for sea clutter suppression and target detection based on singular spectrum decomposition according to claim 1, wherein in S7.2
Where m=n- (M-1) τ.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IL85013A0 (en) * | 1987-01-23 | 1988-06-30 | Hughes Aircraft Co | Helicopter recognition radar processor |
| CN108387880A (en) * | 2018-01-17 | 2018-08-10 | 西安大衡天成信息科技有限公司 | Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes |
| CN111580091A (en) * | 2020-05-17 | 2020-08-25 | 西北工业大学 | Sea surface weak target detection method based on AR spectrum singular intensity function |
-
2020
- 2020-09-28 CN CN202011042775.7A patent/CN112162269B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IL85013A0 (en) * | 1987-01-23 | 1988-06-30 | Hughes Aircraft Co | Helicopter recognition radar processor |
| CN108387880A (en) * | 2018-01-17 | 2018-08-10 | 西安大衡天成信息科技有限公司 | Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes |
| CN111580091A (en) * | 2020-05-17 | 2020-08-25 | 西北工业大学 | Sea surface weak target detection method based on AR spectrum singular intensity function |
Non-Patent Citations (1)
| Title |
|---|
| 海杂波AR谱多重分形特性及微弱目标检测方法;范一飞;罗丰;李明;胡冲;陈帅霖;;电子与信息学报(第02期);全文 * |
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