CN115774248A - Sea surface small target detection method and system based on multiple characteristics of graph - Google Patents

Sea surface small target detection method and system based on multiple characteristics of graph Download PDF

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CN115774248A
CN115774248A CN202211513958.1A CN202211513958A CN115774248A CN 115774248 A CN115774248 A CN 115774248A CN 202211513958 A CN202211513958 A CN 202211513958A CN 115774248 A CN115774248 A CN 115774248A
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蒋俊正
梁振传
钱江
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Yangtze River Delta Research Institute of UESTC Huzhou
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Abstract

The invention discloses a sea surface small target detection method and a system based on multiple image characteristics, wherein the method and the system are divided into a training part and a detection part, the training part comprises the steps of preprocessing radar echo data of a training unit, extracting image characteristics from the preprocessed data, constructing image characteristic vectors, and calculating a judgment area by utilizing a convex hull learning algorithm; the detection part comprises preprocessing radar echo data of a unit to be detected, extracting graph features from the preprocessed data, constructing graph feature vectors of the unit to be detected, and judging whether a radar target exists or not by constructing detection statistics through a judgment area and the graph feature vectors of the unit to be detected; the invention extracts three different image characteristics from the sea clutter sequence, and uses the three image characteristics to distinguish the pure clutter data from the echo data containing the target to complete the sea surface target detection.

Description

Sea surface small target detection method and system based on multiple characteristics of graph
Technical Field
The invention relates to the technical field of radar target detection, in particular to a sea surface small target detection method and system based on multiple characteristics of a graph.
Background
In recent years, small sea-surface targets such as boats, yachts, and airplane debris have become important targets for radar warning along with miniaturization and stealth of sea-surface targets. Generally, the radar scattering cross-sectional area (RCS) of a small floating target on the sea surface is weak, the moving speed is slow, and a coherent or incoherent constant false alarm rate detection algorithm for detecting by using echo energy has an obvious performance bottleneck when the small floating target is detected. In order to avoid energy detection, some researchers provide feature-based target detection methods, the methods extract features by means of differences between clutter and target echoes, the extracted features comprise single-fractal and fractional Fourier transform domain fractal and other fractal features, relative average amplitude and time domain information entropy and other time domain features, frequency domain features, time-frequency domain features, polarization features and the like, detectors based on the features all obtain good detection effects to a certain extent, but the features do not consider the relevance among data in the extraction process, so that some key information existing among the data is ignored, and the detection performance is limited.
The graph theory and the graph neural network are used as an effective method for describing the relevance between data, and have wide application in the aspects of computer science, the economic field, discrete signal processing and the like. In order to utilize the amplitude correlation among echo data, a method based on graph theory is greatly introduced into target detection in the background of sea clutter. On volume 57 of IEEE Transactions on Geoscience and Remote Sensing published in 2019, YAN Kun et al propose a method for detecting a small sea surface floating target based on a graph, which directly processes radar echo signals in a time domain, constructs a directed graph by using the amplitude of the echo, calculates the maximum eigenvalue of a Laplace matrix of the directed graph in consideration of the connectivity of edges, obtains a detection threshold through a door-to-door Carlo experiment and completes target detection, and compares the detection with a detector based on a single fractal characteristic, wherein the detection method has excellent performance, especially when the signal-to-noise ratio is small. However, since the graph features extracted by this method are single, and feature decomposition is required when obtaining the eigenvalue of the laplacian matrix, the time required for feature extraction is long, and thus the detection effect is yet to be improved.
Disclosure of Invention
In view of this, the present invention provides a method and a system for detecting a small sea surface target based on multiple features of a map, where the method obtains better target detection performance by using correlation between echo data of a sea clutter radar.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a sea surface small target detection method based on multiple characteristics of a graph, which comprises the following steps:
step 1, firstly, transmitting a pulse signal through a radar transmitter, receiving a multi-pulse signal reflected by the sea surface through a radar receiver, and obtaining radar echo data after the multi-pulse signal is subjected to matching filtering, wherein the echo data is divided into pure clutter echo data and echo data containing a target;
selecting P distance units from pure clutter echo data as training units, the training unit time sequence z p Comprises the following steps: z is a radical of formula p ＝[z p (1),z p (2),…,z p (N)]Wherein, P =1,2, \8230, P is the number of training units, and N is the time sequence length; will train the unit time sequence z p The average is divided into short vectors of length L, i.e.: z is a radical of formula p ＝[z p,1 ,z p,2 ,…,z p,l ,…,z p,N/L ]Wherein P =1,2, \ 8230;, P, z p,l The L short vector representing the training unit time sequence, L =1,2, \8230;, N/L;
selecting a unit r to be detected from echo data containing a target;
step 2, training sheetMeta-time series z p Preprocessing is carried out to obtain a training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000021
I.e. by
Figure BDA0003971023380000022
Wherein P =1,2, \ 8230;, P,
Figure BDA0003971023380000023
representing a time sequence z of training elements p The normalized Doppler power spectrum obtained by the first short vector preprocessing;
step 3, training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000024
Modeling graphs and graph signals in which a time series of elements z is trained p Each short sequence z of p,l Normalized Doppler power spectrum of
Figure BDA0003971023380000025
Two undirected graphs and signals on the graphs can be obtained through modeling, and three graph characteristics are obtained through calculation: graph Laplace regularization η 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) Variance eta of graph vertex in degree 3 (z p,l ) And constructing a map three feature vector by the three features:
η(z p,l )＝[η 1 (z p,l ),η 2 (z p,l ),η 3 (z p,l )] T ，
wherein [ ·] T Representing transposing the matrix; combining P training units and N/L graph three-feature vectors of each training unit into a training sample set
Figure BDA0003971023380000026
Step 4, according to the training sample setClosing box
Figure BDA0003971023380000027
Obtaining a three-dimensional convex hull in a three-dimensional feature space and determining a false alarm probability P f Next, calculating a decision region omega of the detector by using a greedy convex hull learning algorithm;
step 5, calculating the unit r to be detected to obtain graph Laplace regularization eta 1 (r), trace η of the graph Laplace matrix 2 (r) variance η of the in-degree of the graph vertex 3 (r) three graph characteristics, and constructing a graph three characteristic vector eta (r) of the unit to be detected, namely eta (r) = [ eta = 1 (r),η 2 (r),η 3 (r)] T ；
Step 6, calculating the detection statistic of the unit to be detected according to the decision region omega of the detector and the map three characteristic vectors eta (r) of the unit to be detected
Figure BDA0003971023380000031
Based on detection statistics
Figure BDA0003971023380000032
Whether the target exists is judged by the size of (1): if the statistic is detected
Figure BDA0003971023380000033
If the value is larger than zero, the result shows that the map three feature vectors eta (r) of the unit to be detected are outside the decision area of the detector, and a target exists in the unit to be detected, if the detection statistic quantity is
Figure BDA0003971023380000034
And if the result is less than zero, the map three feature vector eta (r) of the unit to be detected is in the decision area of the detector, and no target exists in the unit to be detected.
Further, step 2 is to train unit time sequence z p ＝[z p,1 ,z p,2 ,…,z p,l ,…,z p,N/L ]Carrying out pretreatment, and specifically comprising the following substeps:
2.1 computing short vector z for each training Unit p,l Doppler power spectrum of
Figure BDA0003971023380000035
Figure BDA0003971023380000036
Figure BDA0003971023380000037
Where Δ f is the Doppler frequency sampling interval, T r For the pulse repetition interval, L is the number of FFT sample points, exp () represents an exponential function with e as the base, and k represents the doppler unit;
2.2 calculating the mean spectrum from the P training elements and the N/L Doppler power spectra of each training element
Figure BDA0003971023380000038
Sum standard deviation spectrum
Figure BDA0003971023380000039
Figure BDA00039710233800000310
Figure BDA00039710233800000311
Wherein k = - [ L/2], [ L/2] -1;
2.3 spectra from the mean
Figure BDA00039710233800000312
Standard deviation spectrum
Figure BDA00039710233800000313
And a short vector z for each training unit p,l Doppler power spectrum of
Figure BDA00039710233800000314
Calculating a short vector z for each training unit p,l Normalized Doppler power spectrum of
Figure BDA00039710233800000315
Figure BDA0003971023380000041
Further, in step 3, the training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000042
Modeling of maps and map signals, wherein a time sequence z of training elements is p Short sequence of (a) z p,l Normalized Doppler power spectrum of
Figure BDA0003971023380000043
Modeling into two undirected graphs and signals on the graphs, and calculating three graph characteristics according to the following specific sub-steps:
3.1 pairs of normalized Doppler Power spectra
Figure BDA0003971023380000044
Carrying out maximum and minimum normalization:
Figure BDA0003971023380000045
wherein k = - [ L/2],...,[L/2]-1，
Figure BDA0003971023380000046
The energy data representing the largest and smallest normalized kth doppler cell,
Figure BDA0003971023380000047
3.2 mixing
Figure BDA0003971023380000048
Modeling as an undirected graph
Figure BDA0003971023380000049
Defining a Doppler Unit { - [ L/2 { [ L/2]],...,[L/2]-1} is an undirected graph
Figure BDA00039710233800000410
Get an undirected graph
Figure BDA00039710233800000411
Set of vertices of
Figure BDA00039710233800000412
And considering each vertex to be connected with d adjacent vertexes, wherein the weight value between the connected vertexes is 1, and obtaining the undirected graph
Figure BDA00039710233800000413
Of a neighboring matrix
Figure BDA00039710233800000414
And laplacian matrix
Figure BDA00039710233800000415
Defining the energy data of the k-th Doppler unit after maximum and minimum normalization as a vertex v k Graph signal x of k And will be located in an undirected graph
Figure BDA00039710233800000416
Is represented by
Figure BDA00039710233800000417
3.3 according to quantization Interval 1/Gamma pairs
Figure BDA00039710233800000418
Carrying out uniform quantization:
Figure BDA00039710233800000419
wherein k = - [ L/2],...,[L/2]-1，
Figure BDA00039710233800000420
Representing the quantized energy data of the kth Doppler unit, i representing the quantized value of the energy data of the Doppler unit, and gamma representing the number of quantization levels;
3.4 mixing
Figure BDA00039710233800000421
Modeling as an undirected graph
Figure BDA00039710233800000422
Defining quantized values {0, \8230;, i, \8230;. Gamma } as undirected graphs
Figure BDA00039710233800000423
Get an undirected graph
Figure BDA00039710233800000424
Set of vertices of
Figure BDA00039710233800000425
Defining the vertex corresponding to the quantization value in each Doppler unit to be connected with the vertex corresponding to the quantization value in the adjacent Doppler unit, defining the connection times between two vertexes as the weight between the two vertexes, and obtaining the undirected graph
Figure BDA0003971023380000051
Of a neighboring matrix
Figure BDA0003971023380000052
Figure BDA0003971023380000053
To undirected graph
Figure BDA0003971023380000054
Of (2) an adjacent matrix
Figure BDA0003971023380000055
Summing the elements of each row to obtain an undirected graph
Figure BDA0003971023380000056
In-degree matrix of
Figure BDA0003971023380000057
Figure BDA0003971023380000058
Wherein,
Figure BDA0003971023380000059
representing a vertex v i The degree of entry is calculated to obtain an undirected graph
Figure BDA00039710233800000510
Laplacian matrix of
Figure BDA00039710233800000511
Figure BDA00039710233800000512
3.5 according to undirected graph
Figure BDA00039710233800000513
Laplacian matrix of
Figure BDA00039710233800000514
Undirected graph
Figure BDA00039710233800000515
Signal on
Figure BDA00039710233800000516
Undirected graph
Figure BDA00039710233800000517
Laplacian matrix of
Figure BDA00039710233800000518
And undirected graph
Figure BDA00039710233800000519
In-degree matrix of
Figure BDA00039710233800000520
Three graph features were calculated: graph Laplace regularization η 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) Variance eta of graph vertex in degree 3 (z p,l )：
Figure BDA00039710233800000521
Wherein Tr (·) represents the trace of the matrix;
3.6 Laplace regularization η from the graph 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) Variance eta of graph vertex in degree 3 (z p,l ) And constructing a feature vector of the graph: eta (z) p,l )＝[η 1 (z p,l ),η 2 (z p,l ),η 3 (z p,l )] T 。
4. The method for detecting small sea surface targets based on multiple features of map as claimed in claim 1, wherein in step 4, the method is based on a training sample set
Figure BDA00039710233800000522
At a given false alarm probability P f Next, the specific sub-step of calculating the decision region Ω of the detector by using the greedy convex hull learning algorithm is as follows:
4.1 define a convex hull CH (S) containing all samples of the training sample set S:
Figure BDA00039710233800000523
wherein SP {. Cndot } represents a closed space surrounded by triangles,
Figure BDA00039710233800000524
three vertices representing the qth triangular face constituting the surface of the convex hull CH (S), Q representing the total number of triangular faces on the convex hull CH (S);
4.2 according to a given false alarm probability P f And greedy convex hull learning algorithm, sequentially deleting to minimize convex hull volume
Figure BDA0003971023380000061
A false alarm training sample to obtain a residual training sample set S', wherein [ ·]Represents rounding down;
4.3 forming a convex hull CH (S ') from the remaining training sample set S ' as a decision region Ω = CH (S ') of the detector.
Further, in step 6, according to the decision region Ω = CH (S') of the detector and the map three feature vectors η (r) of the unit to be detected, the detection statistic of the unit to be detected is calculated
Figure BDA0003971023380000062
Figure BDA0003971023380000063
Where det (-) denotes the determinant of the matrix,
Figure BDA0003971023380000064
three vertices of the F-th triangular face constituting the surface of the convex hull CH (S ') are indicated, and F represents the total number of triangular faces on the convex hull CH (S').
The invention provides a sea surface small target detection system based on multiple graph features, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method when executing the program.
The invention has the beneficial effects that:
the invention provides a method and a system for detecting a small sea surface floating target based on multiple characteristics of a graph, which are characterized in that a radar transmitter is used for transmitting a pulse signal, a radar receiver is used for receiving a multi-pulse signal reflected by the sea surface to obtain radar echo data containing a target, and a unit r to be detected is selected; for training unit time sequence z p Preprocessing is carried out to obtain a training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000065
Training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000066
Modeling a graph and a graph signal, calculating to obtain three graph features and constructing a graph three feature vector: obtaining a three-dimensional convex hull in a three-dimensional feature space according to a training sample set and giving a false alarm probability P f Next, calculating a decision region omega of the detector by using a greedy convex hull learning algorithm; calculating three graph characteristics of the unit to be detected, constructing a graph three characteristic vector eta (r) of the unit to be detected, and calculating the detection statistic of the unit to be detected according to the decision region omega of the detector and the graph three characteristic vector eta (r) of the unit to be detected
Figure BDA0003971023380000067
Based on detection statistics
Figure BDA0003971023380000068
Determines the target.
Three different image characteristics are extracted from the sea clutter sequence, and the three image characteristics are combined and utilized to distinguish pure clutter data from echo data containing targets so as to complete sea surface target detection.
The method utilizes the relevance between the sea clutter radar echo data, considers some key information existing between the data, and can obtain better target detection performance compared with the existing multi-feature-based detector. The method considers the correlation among the radar echo data from different angles, carries out composition, extracts a plurality of graph features, avoids the feature decomposition calculation of a matrix, and has higher feature extraction speed and better radar target detection effect.
The method models the normalized Doppler power spectrum into two undirected graphs and signals on the graphs, and jointly utilizes three graph characteristics: the graph Laplace regularization, the variance of the trace of the graph Laplace matrix and the image vertex incoherence consider the relevance among the sea clutter data, and distinguish the pure clutter data from the echo data containing the target.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
Drawings
In order to make the purpose, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
fig. 1 is a flow chart of an implementation of the present invention according to an embodiment.
Figure 2 is a flow chart of modeling the normalized doppler power spectrum of an embodiment as two undirected graphs and an on-graph signal and deriving three graph features.
FIG. 3 is a graph comparing the detection performance of the example with the present invention at four polarizations of the three prior art detectors at an observation time that is multiplied from 0.064 seconds to 4.096 seconds.
Detailed Description
The present invention is further described below in conjunction with the drawings and the embodiments so that those skilled in the art can better understand the present invention and can implement the present invention, but the embodiments are not to be construed as limiting the present invention.
Referring to fig. 1, the method for detecting the small sea surface floating target based on the multiple graph features comprises a training part and a detection part, wherein the training part comprises preprocessing radar echo data of a training unit, extracting graph features from the preprocessed data, constructing graph feature vectors, and calculating a decision area by using a convex hull learning algorithm; the detection part comprises preprocessing radar echo data of a unit to be detected, extracting graph features from the preprocessed data, constructing graph feature vectors of the unit to be detected, and judging whether a radar target exists or not by constructing detection statistics through a judgment area and the graph feature vectors of the unit to be detected; the method comprises the following specific steps:
step 1, firstly, transmitting a pulse signal through a radar transmitter, receiving a multi-pulse signal reflected by the sea surface through a radar receiver, and obtaining radar echo data after the multi-pulse signal is matched and filtered, wherein the echo data is divided into pure clutter echo data and echo data containing a target;
selecting P distance units from pure clutter echo data as training units, the training unit time sequence z p Comprises the following steps: z is a radical of p ＝[z p (1),z p (2),…,z p (N)]Wherein, P =1,2, \8230, P is the number of training units, and N is the time sequence length; will train the unit time sequence z p The average is divided into short vectors of length L, i.e.: z is a radical of formula p ＝[z p,1 ,z p,2 ,…,z p,l ,…,z p,N/L ]Wherein P =1,2, \ 8230;, P, z p,l The L short vector representing the training unit time sequence, L =1,2, \8230;, N/L;
selecting a unit r to be detected from echo data containing a target;
step 2, training unit time sequence z p ＝[z p,1 ,z p,2 ,…,z p,l ,…,z p,N/L ]Short vector z of each training unit in p,l Preprocessing the Doppler signal to obtain a normalized Doppler power spectrum
Figure BDA0003971023380000081
The method comprises the following steps:
2.1 calculating for each training unitShort vector z p,l Doppler power spectrum of
Figure BDA0003971023380000082
Figure BDA0003971023380000083
Figure BDA0003971023380000084
Where Δ f is the Doppler frequency sampling interval, T r For the pulse repetition interval, L is the number of FFT sample points, exp () represents an exponential function with e as the base, and k represents the doppler unit;
2.2 calculating the mean spectrum from the P training units and the N/L Doppler power spectrums of each training unit
Figure BDA0003971023380000085
Sum standard deviation spectrum
Figure BDA0003971023380000086
Figure BDA0003971023380000087
Figure BDA0003971023380000088
Wherein k = - [ L/2], [ L/2] -1;
2.3 spectra from the mean
Figure BDA0003971023380000089
Standard deviation spectrum
Figure BDA00039710233800000810
And a short vector z for each training unit p,l Doppler power spectrum of
Figure BDA00039710233800000811
Computing a short vector z for each training unit p,l Normalized Doppler power spectrum of
Figure BDA00039710233800000812
Figure BDA00039710233800000813
According to the short vector z of each training unit p,l Normalized Doppler power spectrum of
Figure BDA0003971023380000091
Obtaining a training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000092
Wherein P =1,2, \ 8230, and P is the number of training units;
step 3, training unit time sequence z p Normalized Doppler power spectrum of
Figure BDA0003971023380000093
Modelling of graphs and graph signals, in which a time sequence of elements z is trained p Each short sequence z of p,l Normalized Doppler power spectrum of
Figure BDA0003971023380000094
Two undirected graphs and signals on the graphs can be obtained through modeling, and three graph features and a graph three feature vector are obtained through calculation by the two undirected graphs and the signals on the graphs, and the method comprises the following steps with reference to fig. 2:
3.1 pairs of normalized Doppler Power spectra
Figure BDA0003971023380000095
Maximum and minimum normalization is performed:
Figure BDA0003971023380000096
wherein k = - [ L/2],...,[L/2]-1，
Figure BDA0003971023380000097
The energy data representing the largest and smallest normalized kth doppler cell,
Figure BDA0003971023380000098
3.2 mixing
Figure BDA0003971023380000099
Modeling as an undirected graph
Figure BDA00039710233800000910
Defining a Doppler Unit { - [ L/2 { [ L/2]],...,[L/2]-1} is an undirected graph
Figure BDA00039710233800000911
To get an undirected graph
Figure BDA00039710233800000912
Set of vertices of
Figure BDA00039710233800000913
And considering each vertex to be connected with d adjacent vertexes, wherein the weight value between the connected vertexes is 1, and obtaining the undirected graph
Figure BDA00039710233800000914
Of a neighboring matrix
Figure BDA00039710233800000915
And laplacian matrix
Figure BDA00039710233800000916
Defining the energy data of the k-th Doppler unit after maximum and minimum normalization as a vertex v k Graph signal x of k And will be located in an undirected graph
Figure BDA00039710233800000917
Is represented by
Figure BDA00039710233800000918
3.3 according to quantization Interval 1/Gamma pairs
Figure BDA00039710233800000919
Carrying out uniform quantization:
Figure BDA00039710233800000920
wherein k = - [ L/2],...,[L/2]-1，
Figure BDA00039710233800000921
Represents the quantized energy data of the kth Doppler cell, i represents the quantized value of the energy data of the Doppler cell, and gamma represents the number of quantization levels;
3.4 will
Figure BDA0003971023380000101
Modeling as an undirected graph
Figure BDA0003971023380000102
Defining quantized values {0, \8230;, i, \8230;. Gamma } as undirected graphs
Figure BDA0003971023380000103
To get an undirected graph
Figure BDA0003971023380000104
Set of vertices of
Figure BDA0003971023380000105
Defining the vertex corresponding to the quantization value in each Doppler unit to be connected with the vertex corresponding to the quantization value in the adjacent Doppler unit, defining the connection times between two vertexes as the weight between the two vertexes, and obtaining the undirected graph
Figure BDA0003971023380000106
Of a neighboring matrix
Figure BDA0003971023380000107
Figure BDA0003971023380000108
To undirected graph
Figure BDA0003971023380000109
Of (2) an adjacent matrix
Figure BDA00039710233800001010
Summing the elements of each row to obtain an undirected graph
Figure BDA00039710233800001011
In-degree matrix of
Figure BDA00039710233800001012
Figure BDA00039710233800001013
Wherein,
Figure BDA00039710233800001014
representing the vertex v i Calculating to obtain an undirected graph
Figure BDA00039710233800001015
Laplacian matrix of
Figure BDA00039710233800001016
Figure BDA00039710233800001017
3.5 according to undirected graph
Figure BDA00039710233800001018
Laplacian matrix of
Figure BDA00039710233800001019
Undirected graph
Figure BDA00039710233800001020
Signal on
Figure BDA00039710233800001021
Undirected graph
Figure BDA00039710233800001022
Laplacian matrix of
Figure BDA00039710233800001023
And undirected graph
Figure BDA00039710233800001024
In-degree matrix of
Figure BDA00039710233800001025
Three graph features were calculated: graph Laplace regularization η 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) Variance eta of graph vertex in degree 3 (z p,l )：
Figure BDA00039710233800001026
Wherein Tr (·) represents the trace of the matrix;
3.6 regularization η according to Laplace of the graph 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) Variance eta of graph vertex in degree 3 (z p,l ) And constructing a graph three feature vectors: eta (z) p,l )＝[η 1 (z p,l ),η 2 (z p,l ),η 3 (z p,l )] T Therein, [ ·] T Indicating that the matrix is transposed.
Forming a training sample set by P training units and N/L graph three-feature vectors of each training unit
Figure BDA00039710233800001027
In the embodiment, 10000 training samples are taken;
step 4, according to the training sample set
Figure BDA0003971023380000111
Obtaining a three-dimensional convex hull in a three-dimensional feature space and determining a false alarm probability P f Next, a greedy convex hull learning algorithm is used to calculate a decision region Ω of the detector, which includes the following steps:
4.1 define a convex hull CH (S) containing all samples of the training sample set S:
Figure BDA0003971023380000112
wherein SP {. Is } represents a closed space surrounded by triangles,
Figure BDA0003971023380000113
three vertices representing the qth triangular face constituting the surface of the convex hull CH (S), Q representing the total number of triangular faces on the convex hull CH (S);
4.2 according to a given false alarm probability P f And a greedy convex hull learning algorithm, which deletes [ P N/L P ] in turn to make the convex hull volume reduce most f ]A false alarm training sample to obtain a residual training sample set S', wherein [ ·]Represents rounding down;
4.3 compose a convex hull CH (S ') from the remaining training sample set S ' as the decision region Ω = CH (S ') of the detector.
Step 5, utilizing the time sequence z of the unit r to be detected r And the mean spectrum obtained in step 2.2
Figure BDA0003971023380000114
Sum standard deviation spectrum
Figure BDA0003971023380000115
Preprocessing to obtain a time sequence z of a unit to be detected r Normalized Doppler power spectrum of
Figure BDA0003971023380000116
And calculating to obtain graph Laplacian regularization eta of the detection unit 1 (r), trace η of graph Laplace matrix 2 (r) variance η of graph vertex incomes 3 (r) the three map features, and then obtaining a map three feature vector eta (r) of the unit to be detected, namely eta (r) = [ eta (r) = 1 (r),η 2 (r),η 3 (r)] T ；
Step 6, calculating the detection statistic of the unit to be detected according to the decision region omega of the detector and the map three characteristic vectors eta (r) of the unit to be detected
Figure BDA0003971023380000117
Figure BDA0003971023380000118
Wherein det (-) represents the determinant of the matrix,
Figure BDA0003971023380000119
three vertices representing the F-th triangular face constituting the surface of the convex hull CH (S '), F representing the total number of triangular faces on the convex hull CH (S');
and based on the detected statistics
Figure BDA00039710233800001110
Whether the target exists is judged: if the statistic is detected
Figure BDA00039710233800001111
If the value is larger than zero, the result shows that the map three feature vectors eta (r) of the unit to be detected are outside the decision area of the detector, and a target exists in the unit to be detected, if the detection statistic quantity is
Figure BDA00039710233800001112
And if the value is less than zero, the map three feature vectors eta (r) of the unit to be detected are in a decision area of the detector, and no target exists in the unit to be detected.
Based on the steps 1 to 6, the detection of the small floating targets on the sea surface based on the multiple characteristics of the graph is realized.
The system for detecting the small sea surface target based on the multiple features of the graph comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the program to realize the method.
The 10 IPIX radar actual measurement sea clutter data used in the embodiment are collected in 1993 years, the data are current internationally recognized small target actual measurement data sets, the height of the radar erection is 30 meters, the pulse repetition frequency is 1000Hz, and the distance resolution is 30 meters; each set of data comprises four kinds of polarization data, wherein two kinds of polarization data are homopolar data HH and VV, and two kinds of polarization data are cross polarization data HV and VH; each polarization data comprises 14 distance units and the data length is 2 17 The experimental target was a metal sphere of 1 meter diameter. In an experiment, the method, the detector based on graph link density, the detector based on three characteristics and the detector based on fractal characteristics are respectively utilized to detect the sea surface target, and referring to fig. 3, when the observation time is multiplied from 0.064 seconds to 4.096 seconds, the average detection probability of 10 groups of data in each polarization direction is far greater than that of three existing detectors. In conclusion, the method of the present invention has better detection performance than the conventional detectors.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.

Claims (6)

1. The sea surface small target detection method based on the multiple characteristics of the graph is characterized in that: the method comprises the following steps:
step 1, transmitting a pulse signal through a radar transmitter, receiving a multi-pulse signal reflected by the sea surface through a radar receiver, and obtaining radar echo data after the multi-pulse signal is subjected to matching filtering, wherein the echo data is divided into pure clutter echo data and echo data containing a target;
selecting P distance units from pure clutter echo data as training units, the training unit time sequence z p Comprises the following steps: z is a radical of formula p ＝[z p (1),z p (2),…,z p (N)]Wherein, P =1,2, \8230, P is the number of training units, and N is the time sequence length; training unit time sequence z p The average is divided into short vectors of length L, i.e.: z is a radical of p ＝[z p,1 ,z p,2 ,…,z p,l ,…,z p,N/L ]Wherein P =1,2, \ 8230;, P, z p,l The L short vector representing the training unit time sequence, L =1,2, \8230;, N/L;
selecting a unit r to be detected from echo data containing a target;
step 2, training unit time sequence z p Preprocessing is carried out to obtain a training unit time sequence z p Normalized Doppler power spectrum of
Figure FDA0003971023370000011
Namely, it is
Figure FDA0003971023370000012
Wherein P =1,2, \ 8230;, P,
Figure FDA0003971023370000013
representing a time sequence z of training elements p The normalized Doppler power spectrum obtained by the first short vector preprocessing;
step 3, training unit time sequence z p Normalized Doppler power spectrum of
Figure FDA0003971023370000014
Modelling of graphs and graph signals, in which a time sequence of elements z is trained p Each short sequence z of p,l Normalized Doppler power spectrum of
Figure FDA0003971023370000015
Two undirected graphs and signals on the graphs can be obtained through modeling, and three graph characteristics are obtained through calculation: graph Laplace regularization η 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) Variance eta of graph vertex in degree 3 (z p,l ) And constructing a map three feature vector by the three features:
η(z p,l )＝[η 1 (z p,l ),η 2 (z p,l ),η 3 (z p,l )] T ，
wherein [ ·] T Representing transposing the matrix; combining P training units and N/L graph three-feature vectors of each training unit into a training sample set
Figure FDA0003971023370000016
Step 4, according to the training sample set
Figure FDA0003971023370000017
Obtaining a three-dimensional convex hull in a three-dimensional feature space and determining a false alarm probability P f Next, calculating a decision region omega of the detector by using a greedy convex hull learning algorithm;
step 5, calculating the unit r to be detected to obtain graph Laplace regularization eta 1 (r), trace η of graph Laplace matrix 2 (r) variance η of graph vertex incomes 3 (r) three graph characteristics, and constructing a graph three characteristic vector eta (r) of the unit to be detected, namely eta (r) = [ eta = 1 (r),η 2 (r),η 3 (r)] T ；
Step 6, calculating the detection statistic of the unit to be detected according to the decision region omega of the detector and the map three characteristic vectors eta (r) of the unit to be detected
Figure FDA0003971023370000021
Based on detection statistics
Figure FDA0003971023370000022
Whether the target exists is judged: if the statistic is detected
Figure FDA0003971023370000023
If the value is larger than zero, the result shows that the map three feature vectors eta (r) of the unit to be detected are outside the decision area of the detector, and a target exists in the unit to be detected, if the detection statistic quantity is
Figure FDA0003971023370000024
And if the value is less than zero, the map three feature vectors eta (r) of the unit to be detected are in a decision area of the detector, and no target exists in the unit to be detected.
2. The method as claimed in claim 1, wherein the training unit time sequence z is processed in step 2 p ＝[z p,1 ,z p,2 ,…,z p,l ,…,z p,N/L ]Performing pretreatment, wherein the specific substeps are as follows:
2.1 computing short vector z for each training Unit p,l Doppler power spectrum of
Figure FDA0003971023370000025
Figure FDA0003971023370000026
Figure FDA0003971023370000027
k＝-[L/2],...,[L/2]-1,
Where Δ f is the Doppler frequency sampling interval, T r For the pulse repetition interval, L is the number of FFT sample points, exp () represents an exponential function with e as the base, and k represents the doppler unit;
2.2 training units based on PAnd calculating the mean spectrum of N/L Doppler power spectrums of each training unit
Figure FDA0003971023370000028
Sum standard deviation spectrum
Figure FDA0003971023370000029
Figure FDA00039710233700000210
Figure FDA00039710233700000211
Wherein k = - [ L/2], [ L/2] -1;
2.3 spectra from the mean
Figure FDA00039710233700000212
Standard deviation spectrum
Figure FDA00039710233700000213
And a short vector z for each training unit p,l Doppler power spectrum of
Figure FDA00039710233700000214
Calculating a short vector z for each training unit p,l Normalized Doppler power spectrum of
Figure FDA00039710233700000215
Figure FDA0003971023370000031
3. The method for detecting small sea surface targets based on multiple features of map as claimed in claim 1, characterized by the steps ofTraining unit time sequence z in step 3 p Normalized Doppler power spectrum of
Figure FDA0003971023370000032
Modeling graphs and graph signals, wherein a training unit time sequence z p Short sequence of (a) z p,l Normalized Doppler power spectrum of
Figure FDA0003971023370000033
Modeling into two undirected graphs and signals on the graphs, and calculating three graph characteristics according to the following specific sub-steps:
3.1 pairs of normalized Doppler Power spectra
Figure FDA0003971023370000034
Carrying out maximum and minimum normalization:
Figure FDA0003971023370000035
k＝-[L/2],...,[L/2]-1,
wherein k = - [ L/2],...,[L/2]-1，
Figure FDA0003971023370000036
The energy data representing the largest and smallest normalized kth doppler cell,
Figure FDA0003971023370000037
3.2 mixing
Figure FDA0003971023370000038
Modeling as an undirected graph
Figure FDA0003971023370000039
Defining a Doppler Unit { - [ L/2 { [ L/2]],...,[L/2]-1 is an undirected graph
Figure FDA00039710233700000310
Get undirectedDrawing (A)
Figure FDA00039710233700000311
Set of vertices of
Figure FDA00039710233700000312
And considering each vertex to be connected with d adjacent vertexes, wherein the weight value between the connected vertexes is 1, and obtaining the undirected graph
Figure FDA00039710233700000313
Of a neighboring matrix
Figure FDA00039710233700000314
And laplacian matrix
Figure FDA00039710233700000315
Defining the energy data of the k-th Doppler unit after maximum and minimum normalization as a vertex v k Graph signal x of k And will be located in an undirected graph
Figure FDA00039710233700000316
Is represented by
Figure FDA00039710233700000317
3.3 according to quantization Interval 1/Gamma pairs
Figure FDA00039710233700000318
Carrying out uniform quantization:
Figure FDA00039710233700000319
wherein k = - [ L/2],...,[L/2]-1，
Figure FDA00039710233700000320
Represents the quantized energy data of the kth Doppler cell, i representsA quantized value of the energy data of the doppler cell, γ representing a quantization level number;
3.4 mixing
Figure FDA00039710233700000321
Modeling as an undirected graph
Figure FDA00039710233700000322
Defining quantized values {0, \8230;, i, \8230;. Gamma } as undirected graph
Figure FDA00039710233700000323
To get an undirected graph
Figure FDA00039710233700000324
Set of vertices of
Figure FDA00039710233700000325
Defining the vertex corresponding to the quantization value in each Doppler unit to be connected with the vertex corresponding to the quantization value in the adjacent Doppler unit, defining the connection times between two vertexes as the weight between the two vertexes, and obtaining the undirected graph
Figure FDA0003971023370000041
Of a neighboring matrix
Figure FDA0003971023370000042
Figure FDA0003971023370000043
To undirected graph
Figure FDA0003971023370000044
Of a neighboring matrix
Figure FDA0003971023370000045
Is summed to obtainUndirected graph
Figure FDA0003971023370000046
In-degree matrix of
Figure FDA0003971023370000047
Figure FDA0003971023370000048
Wherein,
Figure FDA0003971023370000049
representing the vertex v i The degree of entry is calculated to obtain an undirected graph
Figure FDA00039710233700000410
Laplacian matrix of
Figure FDA00039710233700000411
Figure FDA00039710233700000412
3.5 according to undirected graph
Figure FDA00039710233700000413
Laplacian matrix of
Figure FDA00039710233700000414
Undirected graph
Figure FDA00039710233700000415
Signal on
Figure FDA00039710233700000416
Undirected graph
Figure FDA00039710233700000417
Laplacian matrix of
Figure FDA00039710233700000418
And undirected graph
Figure FDA00039710233700000419
In-degree matrix of
Figure FDA00039710233700000420
Three graph features were calculated: graph Laplace regularization η 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) And the variance eta of the figure vertex in-degree 3 (z p,l )：
Figure FDA00039710233700000421
Wherein Tr (·) represents the trace of the matrix;
3.6 regularization η according to Laplace of the graph 1 (z p,l ) Trace η of graph laplace matrix 2 (z p,l ) And the variance eta of the figure vertex in-degree 3 (z p,l ) And constructing a feature vector of the graph: eta (z) p,l )＝[η 1 (z p,l ),η 2 (z p,l ),η 3 (z p,l )] T 。
4. The method for detecting sea surface small targets based on multiple graph features according to claim 1, wherein in step 4, the method is based on a training sample set
Figure FDA00039710233700000422
At a given false alarm probability P f Next, the specific sub-steps of calculating the decision region Ω of the detector by using the greedy convex hull learning algorithm are as follows:
4.1 define a convex hull CH (S) containing all samples of the training sample set S:
Figure FDA00039710233700000423
wherein SP {. Cndot } represents a closed space surrounded by triangles,
Figure FDA0003971023370000051
three vertices representing the qth triangular face constituting the surface of the convex hull CH (S), Q representing the total number of triangular faces on the convex hull CH (S);
4.2 according to a given false alarm probability P f And a greedy convex hull learning algorithm, which deletes [ P N/L P ] in turn to make the convex hull volume reduce most f ]A false alarm training sample to obtain a residual training sample set S', wherein [ ·]Represents rounding down;
4.3 compose a convex hull CH (S ') from the remaining training sample set S ' as the decision region Ω = CH (S ') of the detector.
5. The method for detecting small sea surface targets based on multiple map features as claimed in claim 1, wherein in step 6, the detection statistic of the unit to be detected is calculated according to the decision region Ω = CH (S') of the detector and the map three feature vector η (r) of the unit to be detected
Figure FDA0003971023370000052
Figure FDA0003971023370000053
Wherein det (-) represents the determinant of the matrix,
Figure FDA0003971023370000054
three vertices of the F-th triangular face constituting the surface of the convex hull CH (S ') are indicated, and F represents the total number of triangular faces on the convex hull CH (S').
6. Sea surface small target detection system based on multiple features of the figure, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1 to 5 when executing the program.
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* Cited by examiner, † Cited by third party
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