CN112162269A - Sea clutter suppression and target detection method based on singular spectrum decomposition - Google Patents
Sea clutter suppression and target detection method based on singular spectrum decomposition Download PDFInfo
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Abstract
A sea clutter suppression and target detection method based on Singular Spectral Analysis (SSA) includes firstly calculating an autocorrelation function of echo data, taking a first zero-crossing position of the autocorrelation function as a dimension of a reconstructed radar echo data track matrix, constructing a Toeplitz matrix of the echo data, solving the number of Principal Components (PCA), and judging whether an echo contains a target or not according to the number of PCAs; then, performing singular spectrum decomposition on the radar echo, reconstructing a time sequence corresponding to a principal Component Analysis (MCA) and a Minor Component Analysis (MCA), and when the echo contains a target, taking the time sequence corresponding to the maximum principal Component and omitting the time sequences corresponding to other components; on the contrary, the time sequence corresponding to the minimum secondary component is selected, and the time sequences corresponding to other components are omitted, the processing process can be regarded as self-adaptive filtering independent of an echo statistical model, the sea clutter intensity in the processed radar echo data is effectively suppressed, and the detection probability of the subsequent weak and small targets is favorably improved.
Description
Technical Field
The invention belongs to the technical field of target detection, relates to target detection under a sea clutter background, and provides a sea clutter suppression and target detection method based on singular spectrum decomposition.
Background
Sea clutter refers to echoes generated when a sea observation radar beam irradiates on the sea, the characteristics of the sea clutter are closely related to factors such as the area covered by the radar beam, the height of sea waves (sea condition), the radar working dominant frequency and the bandwidth, and the like, generally, the sea clutter reflects the movement characteristics of wave discharging caused by gravity and the micro-movement characteristics of tubular fine waves caused by sea surface tension, namely, the sea clutter comprises 2 main components, parameters such as the amplitude and the bandwidth of the sea clutter present non-stable and non-linear characteristics along with the change of the sea condition, as shown in (a) and (b) in figure 1, a time domain amplitude diagram of sea clutter data (http:/soma.ec.mcaster.ca/IPIX/dartmouth/datacut.html) measured by an IPIX radar of the Canada McMaster university exists obvious peaks, and the intensity and the number of the peaks increase along with the increase of the sea condition, and figure 2 is a frequency spectrum diagram of the sea clutter data, as shown in (a) and (b), it can be seen that the bandwidth is significantly broadened due to the increased micromotion characteristics caused by the sea surface tension waves in the high sea state. The research on the method for detecting the small targets under the sea clutter background is always a hotspot in the field of radar signal processing, is used for detecting sea surface floaters (floating ice, small ships, aircraft remains and the like) in the civil field, and provides technical support for civil ship navigation, maritime search and rescue and the like. In the military field, the method is used for detecting ships and submarine periscopes with stealth characteristics on the sea, aircrafts flying close to the sea surface and the like, the traditional method regards sea clutter as a complex stable random process, a probability model in statistical significance 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 target detection is further realized by utilizing a mature detection method. However, the assumption of the stationarity of the sea clutter is based on the short observation time, in order to improve the detection probability of the weak and small targets, the radar has to increase the observation time of the target region to improve the target echo energy after coherent accumulation, but the sea clutter will no longer be a stationary random process with the increase of the observation time, as shown in (a) and (b) in fig. 3, the shape parameter k and the scale parameter sigma of the Pareto distribution parameter of the south africa CSIR TFC15_023.01 sea clutter data set fluctuate randomly with the difference of distance units, and if a fixed model parameter is adopted, a larger measurement error is inevitably caused.
In summary, in order to detect a marine weak and small target, the sea observation radar generally increases the observation time of a sea target region to enhance the echo energy of the target, but the strength of the sea echo (sea clutter) received within a large coherent processing time is also correspondingly enhanced, and exhibits non-linearity, non-gaussian and non-stationary characteristics.
In order to solve the above problems, experts and scholars in the field of radar engineering have conducted a great deal of research work, and have had a certain number of research achievements. One of the classical solutions is to dynamically track the statistical characteristics of radar echoes, and perform segmentation processing on the radar echoes according to the principle that the characteristics are similar, so that some problems are solved on the engineering level, but with the improvement of sea conditions, the radar echo data are shorter and shorter in segmentation, and finally have no essential difference with the situation of short-time observation, so that a high false alarm probability and a low discovery probability still exist when weak and small targets are detected, therefore, a signal processing method based on the statistical characteristics does not meet the requirements of engineering under the condition of high sea conditions, and a sea clutter suppression method independent of a statistical model must be searched, so that the problem is solved.
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 position of the autocorrelation function as the dimensions of a Toeplitz matrix and a track matrix of reconstructed radar echo data, further obtaining the number of Principal Components (PCA), and judging whether an echo contains a target or not according to the number of PCA; and then, self-adaptive filtering is carried out on the radar echo by utilizing singular spectrum decomposition, and Constant False Alarm Rate (CFAR) target detection is carried out on the processed radar echo.
In order to achieve the purpose, the invention adopts the technical scheme that:
a sea clutter suppression and target detection method based on singular spectrum decomposition comprises the following steps:
s1) for coherent radar returns, S2) is performed in order, and for incoherent radar returns, S3);
s2) the absolute value of the data sequence of the radar echo I, Q is obtained;
s3) calculating an autocorrelation function value of the echo data;
s4) calculating a first zero-crossing position of an echo data autocorrelation function, and estimating the correlation time of echo data as the dimension of a Toeplitz matrix C for constructing the echo data and the window length L of subsequent singular spectral decomposition;
s5) converting the radar echo data into a Toeplitz matrix C according to the window length L;
s6) carrying out eigenvalue decomposition on the Toeplitz matrix C, and extracting eigenvalue sigmaiAnd corresponding feature vectors viI 1,2, … L, calculating the singular spectrum
Successively adding RiR corresponding to an accumulated value of 0.9 or moreiNumber N ofpIs a principal component σipThe number of (2); definition of σipBeing principal components of radar echo data, vipIs its feature vector, ip is 1,2, …, Np(ii) a Definition of σjmAs a minor component of radar echo data, vjmIs its feature vector, jm is 1,2, …, L-Np；
In principal component σipSub-component σjmAnd calculating after determination:
s6.1 Signal to noise ratio SCR (signal to timer ratio)
S6.2 Signal-to-noise ratio SNR (signal to noise ratio)
S6.3, if the echo only contains the sea clutter, calculating the noise to noise ratio CNR (clutter to noise ratio)
S7) performing a singular spectral decomposition on the radar echo, comprising the steps of:
s7.1: rearranging V-V (V) according to corresponding eigenvalues from large to small for the eigenvector extracted in S6)ip,vjm) (ii) a According to the accumulation result of the singular spectrum, after a Principal Component Analysis (PCA) and a Minor Component Analysis (MCA) are distinguished, the sequences are respectively arranged from large to small, and the operation here is to correspondingly adjust the sequence of the eigenvectors, namely, the eigenvectors 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, changing the radar echo X to { X ═ XkConverting the data into a 2-dimensional trajectory matrix H according to a delay coordinate phase space reconstruction (delay coordinate phase space reconstruction) method, wherein k is 1,2, … N, and N is the radar echo data length; a corresponding embedding dimension (embedding dimension) m ═ L, and a delay time (time delay) τ ═ 1;
where the number of rows M of the matrix H is N- (M-1) τ.
S7.3, calculating the projection W of the 2-dimensional track matrix H on the feature space formed by the spread feature vectors to be HV, and decomposing W into N corresponding to the main component according to the feature vectors corresponding to the main component and the secondary componentpSub-matrix WipCorresponding to the minor component (L-N)p) Sub-matrix WjmTwo sets of matrices;
s7.4, respectively aligning NpSub-matrix WipAnd (L-N)p) Sub-matrix WjmThe inverse diagonal element averaging process is implemented to convert it into a one-dimensional time series Xp＝{xip}, and Xm＝{xjmObtaining time sequences corresponding to the primary and secondary components;
s8) number of principal components N of radar echop>EtPreserving the maximum principal component σipCorresponding time series xipWhen ip is 1, discarding the time series corresponding to other components; if N is presentp≤EtThen X is retainedm＝{xjmThe last time sequence in the sequence, namely the time sequence corresponding to the minimum secondary component, discards the time sequences corresponding to other components; wherein EtA threshold for judging whether the radar echo contains a target or not;
preferably, said EtAnd 2, judging whether the radar echo contains a target threshold, namely, the number of main components contained in the sea clutter is not more than 2, and the 2 main components respectively reflect the gravity wave and the sea surface tension wave strength of the sea wave.
S9) processing radar echoes received within coherent processing time in sequence from S2) to S8), arranging reserved time sequences into a 2-dimensional echo matrix according to echo pulse arrival time, and then performing target detection by using a Constant False Alarm Rate (CFAR) method; the whole radar echo processing process 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 independent of a sea clutter statistical model, which can detect weak and small targets which cannot be found by the traditional method under the given sea clutter background and obviously improve the detection precision.
Drawings
FIG. 1 shows measured sea clutter for an IPIX radar, wherein (a) is low sea clutter and (b) is high sea clutter.
Fig. 2 shows the measured sea clutter spectrum of the IPIX radar, where (a) is the low sea clutter spectrum and (b) is the high sea clutter spectrum.
Fig. 3 shows Pareto distribution parameters of the south africa CSIR TFC15_023.01 sea clutter data set, where (a) is a shape parameter k, and (b) is a scale parameter sigma.
FIG. 4 is a flow chart of the present invention.
Figure 5 shows the target vessel echo in range unit 11 after SSA processing.
FIG. 6 shows the sea clutter in range bin 11 after SSA processing, where (a) is the principal component σ2Corresponding time series, (b) is σ3Corresponding time series.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
The invention is a sea clutter suppression and target detection method based on Singular Spectral Analysis (SSA), calculate the autocorrelation function of the echo data at first, regard first zero crossing position of the autocorrelation function as the dimension of the data path matrix of reconstructed radar echo, construct Toeplitz matrix of the echo data, solve the number of Principal Component Analysis (PCA), judge whether to include the target in the echo according to the number of PCA; then, performing singular spectrum decomposition on the echo track matrix, reconstructing a time sequence corresponding to a principal Component and a Minor Component (MCA), and when the echo contains a target, taking the time sequence corresponding to the maximum principal Component and omitting the time sequences corresponding to other components; on the contrary, the time sequence corresponding to the minimum secondary component is selected, and the time sequences corresponding to other components are omitted, the processing process can be regarded as self-adaptive filtering independent of an echo statistical model, the sea clutter intensity in the processed radar echo data is effectively suppressed, and the detection probability of the subsequent weak and small targets is favorably improved.
The specific steps of the present invention are shown in fig. 4, and include:
s1) for coherent radar returns yi＝vi+juiS2) for incoherent radar returns xiS3), i ═ 1,2, … N, vi、uiAre respectively radar loopsA real part I sequence and an imaginary part Q sequence of the wave data;
s2) the absolute value of the data sequence of the radar echo I, Q is obtained;
s3) calculating an autocorrelation function value of the echo data:
s4) calculating a first zero-crossing position of an echo data autocorrelation function as the dimension of a Toeplitz matrix C for constructing echo data and the window length L of subsequent singular spectral decomposition;
s5) converting the radar echo data into a Toeplitz matrix C according to the window length L;
s6) performing eigenvalue decomposition on the Toeplitz matrix C, namely [ V, D]Eig (c) (Matlab function), the characteristic value σ is extracted from the diagonal of DiAnd corresponding feature vectors viI 1,2, … L, calculating the singular spectrum
Successively adding RiR corresponding to an accumulated value of 0.9 or moreiNumber N ofpIs a principal component σipThe number of (2); definition of σipBeing principal components of radar echo data, vipIs its feature vector, ip is 1,2, …, Np(ii) a Definition of σjmAs a minor component of radar echo data, vjmIs its feature vector, jm is 1,2, …, L-Np(ii) a Based on the conclusions from the principal component analysis of sea clutter, let EtThe threshold that whether the radar echo contains the target is judged for 2, and the main component number that contains of sea clutter is not more than 2 promptly, and 2 main components reflect the gravity wave and the sea tension wave intensity of wave respectively. In principal component σipSub-component σjmAfter determination, it is possible to calculate:
s6.1 Signal to noise ratio SCR (signal to timer ratio)
S6.2 Signal-to-noise ratio SNR (signal to noise ratio)
S6.3, if the echo only contains the sea clutter, calculating the noise to noise ratio CNR (clutter to noise ratio)
S7) performing a singular spectral decomposition on the radar echo, comprising the steps of:
s7.1: rearranging V-V (V) according to corresponding eigenvalues from large to small for the eigenvector extracted in S6)ip,vjm) (ii) a According to the accumulation result of the singular spectrum, after a Principal Component Analysis (PCA) and a Minor Component Analysis (MCA) are distinguished, the sequences are respectively arranged from large to small, and the operation here is to correspondingly adjust the sequence of the eigenvectors, namely, the eigenvectors 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, changing the radar echo X to { X ═ XkConverting the data into a 2-dimensional trajectory matrix H according to a delay coordinate phase space reconstruction (delay coordinate phase space reconstruction) method, wherein k is 1,2, … N, and N is the radar echo data length; a corresponding embedding dimension (embedding dimension) m ═ L, and a delay time (time delay) τ ═ 1;
where M denotes the number of rows of the matrix H, and M ═ N- (M-1) τ.
S7.3, calculating the projection W of the 2-dimensional track matrix H on the feature space formed by the spread feature vectors as HV, and according to the feature directions corresponding to the primary component and the secondary componentDecomposition of the quantity, W, into N corresponding to the principal componentpSub-matrix WipCorresponding to the minor component (L-N)p) Sub-matrix WjmTwo sets of matrices;
s7.4, respectively aligning NpSub-matrix WipAnd (L-N)p) Sub-matrix WjmThe inverse diagonal element averaging process is implemented to convert it into a one-dimensional time series Xp＝{xip}, and Xm＝{xjmObtaining time sequences corresponding to the primary and secondary components;
s8) number of principal components N of radar echop>EtPreserving the maximum principal component σipCorresponding time series xipWhen ip is 1, discarding the time series corresponding to other components; if N is presentp≤EtThen X is retainedm＝{xjmThe last time sequence in the sequence, namely the time sequence corresponding to the minimum secondary component, discards the time sequences corresponding to other components; wherein EtA threshold for judging whether the radar echo contains a target or not;
as shown in fig. 5, the echo of the target ship in the south africa CSIR TFC15_023.01 sea clutter data set after SSA processing by the distance unit 11; FIG. 6 shows the sea clutter separated by the distance cell, where (a) is the principal component σ2Corresponding time series, (b) is σ3Corresponding time series.
S9) processing radar echoes received within coherent processing time in sequence from S2) to S8), arranging reserved time sequences into a 2-dimensional echo matrix according to echo pulse arrival time, and then performing target detection by using a Constant False Alarm Rate (CFAR) method; the whole radar echo processing process is defined as SSA-CFAR detection.
A south Africa CSIR TFC 15-023.01 sea clutter data set is used as a processing object, the data set comprises echo I-Q data of 31 distance units, the data length is 33001, a distance unit 11 comprises an echo of a target ship, the distance units 9-10 and 12-13 are interference areas formed by the target ship and the sea surface, after processing through S2) -S8), the CA-CFAR and the OS-CFAR are respectively adopted to detect targets, the number of training distance units is set to be 16, the number of protection distance units is set to be 2, and the number of targets is set to be 3The false alarm probability is set to Pfa_goal＝10-3Target detection scanning distance units 9-13, discovery probability P of CA-CFARd0.9876, false alarm probability Pfa＝3.0302×10-5(ii) a Discovery probability P of OS-CFARd0.9907, false alarm probability Pfa＝1.2121×10-4。
To verify the advancement of the present invention, the target detection is directly performed on the above data set, and the discovery probability P of CA-CFARd0.1857, false alarm probability Pfa＝6.0604×10-5(ii) a Discovery probability P of OS-CFARd0.3268, false alarm probability Pfa＝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 returns, S2) is performed in order, and for incoherent radar returns, S3);
s2) the absolute value of the data sequence of the radar echo I, Q is obtained;
s3) calculating an autocorrelation function value of the echo data;
s4) calculating a first zero-crossing position of an echo data autocorrelation function as the dimension of a Toeplitz matrix C for constructing echo data and the window length L of subsequent singular spectral decomposition;
s5) converting the radar echo data into a Toeplitz matrix C according to the window length L;
s6) carrying out eigenvalue decomposition on the Toeplitz matrix C, and extracting eigenvalue sigmaiAnd corresponding feature vectors viI 1,2, … L, calculating the singular spectrum
Successively adding RiR corresponding to an accumulated value of 0.9 or moreiNumber N ofpIs a principal component σipThe number of (2); definition of σipBeing principal components of radar echo data, vipIs its feature vector, ip is 1,2, …, Np(ii) a Definition of σjmAs a minor component of radar echo data, vjmIs its feature vector, jm is 1,2, …, L-Np；
S7) performing a singular spectral decomposition on the radar echo, comprising the steps of:
s7.1: rearranging V-V (V) according to corresponding eigenvalues from large to small for the eigenvector extracted in S6)ip,vjm)；
S7.2, changing the radar echo X to { X ═ XkConverting the data into a 2-dimensional track matrix H according to a delay coordinate phase space reconstruction method, wherein k is 1,2, … N, and N is the length of radar echo data; the corresponding embedding dimension m ═ L, delay time τ ═ 1;
s7.3, calculating the projection W of the 2-dimensional track matrix H on the feature space formed by the spread feature vectors to be HV, and decomposing W into N corresponding to the main component according to the feature vectors corresponding to the main component and the secondary componentpSub-matrix WipCorresponding to the minor component (L-N)p) Sub-matrix WjmTwo sets of matrices;
s7.4, respectively aligning NpSub-matrix WipAnd (L-N)p) Sub-matrix WjmThe inverse diagonal element average processing is implemented to convert the same into a one-dimensional time sequence Xp＝{xip}, and Xm＝{xjmObtaining time sequences corresponding to the primary and secondary components;
s8) number of principal components N of radar echop>EtPreserving the maximum principal component σipCorresponding time series xipWhen ip is 1, discarding the time series corresponding to other components; if N is presentp≤EtThen X is retainedm＝{xjmThe last time sequence in the sequence, namely the time sequence corresponding to the minimum secondary component, discards the time sequences corresponding to other components; wherein EtA threshold for judging whether the radar echo contains a target or not;
s9) sequentially carrying out S2) -S8) processing on the radar echoes received within 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 process is defined as SSA-CFAR detection.
2. The method for suppressing sea clutter and detecting targets based on singular spectral decomposition as claimed in claim 1, wherein in S4), the window length L of the singular spectral decomposition is determined by estimating the correlation time of the echo data by calculating the first zero-crossing point of the autocorrelation function of the echo data.
3. The singular spectral decomposition-based sea clutter suppression and target detection method according to claim 1, wherein in S6), in the principal component σipSub-component σjmAnd calculating after determination:
s6.1 Signal-to-noise ratio SCR
S6.2 SNR
S6.3, if the echo only contains the sea clutter, calculating the noise-to-noise ratio CNR
4. The singular spectral decomposition-based sea clutter suppression and target detection method according to claim 1, wherein said E istAnd 2, judging whether the radar echo contains a target threshold, namely, the number of main components contained in the sea clutter is not more than 2, and the 2 main components respectively reflect the gravity wave and the sea surface tension wave strength of the sea wave.
5. The singular spectrum decomposition based sea clutter suppression and target detection method according to claim 1, wherein in S7.2
Wherein M ═ N- (M-1) τ.
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| CN113447901A (en) * | 2021-05-17 | 2021-09-28 | 西安大衡天成信息科技有限公司 | Sea clutter recognition and target detection method independent of probability model |
| CN113447901B (en) * | 2021-05-17 | 2023-11-17 | 西安大衡天成信息科技有限公司 | Sea clutter identification and target detection method independent of probability model |
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