Polarization rotation domain feature extraction and target detection method and device for reduced polarization radar

Abstract

The present application relates to a method and device for extracting polarization rotation domain features and detecting targets of a reduced polarization radar. The method comprises: reconstructing the reduced polarization covariance matrix by pseudo-full polarization to obtain a pseudo-full polarization covariance matrix and a pseudo-full polarization coherence matrix; rotating the pseudo-full polarization covariance matrix and the pseudo-full polarization coherence matrix in the direction of the line of sight of the polarization radar to obtain a rotated covariance matrix and a rotated coherence matrix; constructing a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern based on Pauli vectors and Lexicographic vectors; parameterizing the reduced polarization channel coherence pattern and the reduced polarization channel correlation pattern, and selecting a specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios from the reduced polarization rotation domain features to perform reduced polarization radar target detection. The use of this method can improve the accuracy of target detection.

Description

Technical Field

[0001] The present application relates to the field of radar polarization information processing and application technology, and in particular to a method, device, computer equipment and storage medium for extracting polarization rotation domain features and detecting targets of a reduced polarization radar. Background Art

[0002] Compact polarimetric radar transmits a specific form of electromagnetic waves and receives electromagnetic waves in two orthogonal polarization states. It can maintain a large width while obtaining relatively rich polarization information of the target. It plays an important role in the fields of ground object classification, characteristic parameter inversion, target detection and identification. It is an important sensor in many important fields such as earth observation and sea surface monitoring, and has been widely used.

[0003] Compared with full polarimetric radar, the design and maintenance complexity of the system of compact polarimetric radar is lower and the imaging range is larger, but at the same time, there is still insufficient polarization information. Therefore, the application of compact polarimetric radar is very limited. Fully exploring the polarimetric scattering characteristics contained in the compact polarimetric data and accurately characterizing the polarimetric scattering characteristics of the target are expected to expand the application of compact polarimetric radar, which is of great significance to improving the target detection performance of compact polarimetric radar. Promote the further development of compact polarimetric radar system. The backscattering characteristics of radar targets are sensitive to the relative geometric relationship between the target posture and the polarimetric radar line of sight. For the same target, when its posture relative to the polarimetric radar line of sight is different, its polarimetric scattering characteristics may be significantly different. This phenomenon causes many inconveniences to the processing and application of radar polarimetric information, and is one of the technical bottlenecks faced by the current fine interpretation and quantitative application of the polarimetric scattering mechanism of polarimetric radar targets. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for polarization rotation domain feature extraction and target detection of a reduced polarization radar, which can fully explore the polarization scattering characteristics contained in the reduced polarization data and improve the accuracy of target detection of the reduced polarization radar, in order to address the above technical problems.

[0005] A method for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar, the method comprising:

[0006] The double-channel target backscatter echo vector is obtained according to the reduced polarimetric radar, and the reduced polarimetric covariance matrix is ​​constructed by using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector.

[0007] Perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix;

[0008] Discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence;

[0009] In the direction of the line of sight of the polarized radar, the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix are rotated to obtain a rotated covariance matrix and a rotated coherence matrix;

[0010] The reduced polarization channel coherence pattern and reduced polarization channel correlation pattern are constructed according to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector;

[0011] The coherent pattern of the reduced polarization channel and the correlation pattern of the reduced polarization channel are parameterized to obtain the characteristics of the reduced polarization rotation domain.

[0012] A specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios are selected from the reduced polarization rotation domain features for reduced polarization radar target detection.

[0013] In one embodiment, performing pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix includes:

[0014] The pseudo full polarization covariance matrix is ​​reconstructed by pseudo full polarization to obtain the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix; the pseudo full polarization covariance matrix is The coherence matrix is

[0015] In one embodiment, the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix are rotated in the line of sight direction of the polarization radar to obtain a rotated covariance matrix and a rotated coherence matrix, including:

[0016] In the direction of the polarization radar line of sight, the pseudo full polarization covariance matrix is ​​rotated to obtain the rotated covariance matrix:

[0017]

[0018] In the direction of the polarization radar line of sight, the pseudo full polarization coherence matrix is ​​rotated to obtain the rotated coherence matrix:

[0019]

[0020] Among them, R 3C (θ) and R 3T (θ) are the rotation matrices of the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix, respectively. T For transposition processing.

[0021] In one embodiment, the Pauli vector is Lexicographic vector

[0022] In one embodiment, constructing a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to a discretized rotation angle sequence, a rotation covariance matrix, a rotation coherence matrix, a Pauli vector, and a Lexicographic vector includes:

[0023] According to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector, four independent reduced polarization channel coherence patterns are constructed:

[0024]

[0025]

[0026]

[0027]

[0028] Among them, <·> is the ensemble average processing, |·| is the absolute value processing, and θ represents the rotation angle.

[0029] In one embodiment, four independent reduced polarization channel correlation patterns are constructed according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector:

[0030]

[0031]

[0032]

[0033]

[0034] In one embodiment, the simplified polarization rotation domain features include original simplified polarization related features, maximum simplified polarization related features, minimum simplified polarization related features, simplified polarization correlation, simplified polarization related fluctuation, simplified polarization related contrast, simplified polarization related anti-entropy, simplified polarization related directional pattern maximization rotation angle, simplified polarization related directional pattern minimization rotation angle and simplified polarization related directional pattern width.

[0035] A device for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar, the device comprising:

[0036] Constructing a reduced polarization covariance matrix module, which is used to obtain a dual-channel target backscatter echo vector according to a reduced polarization radar, and constructing a reduced polarization covariance matrix using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector;

[0037] The pseudo full polarization reconstruction and rotation module is used to perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix; discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence; rotate the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix in the direction of the polarization radar line of sight to obtain a rotated covariance matrix and a rotated coherence matrix;

[0038] A coherence pattern and correlation pattern construction module is used to discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence; and to construct a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector;

[0039] A parameterized characterization module is used to parameterize the coherent directional pattern of the reduced polarization channel and the correlation directional pattern of the reduced polarization channel to obtain the characteristics of the reduced polarization rotation domain;

[0040] The target detection module is used to select a specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios from the reduced polarization rotation domain features to perform reduced polarization radar target detection.

[0041] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] The double-channel target backscatter echo vector is obtained according to the reduced polarimetric radar, and the reduced polarimetric covariance matrix is ​​constructed by using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector.

[0043] Perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix;

[0044] Discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence;

[0045] In the direction of the line of sight of the polarized radar, the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix are rotated to obtain a rotated covariance matrix and a rotated coherence matrix;

[0046] The reduced polarization channel coherence pattern and reduced polarization channel correlation pattern are constructed according to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector;

[0047] The coherent pattern of the reduced polarization channel and the correlation pattern of the reduced polarization channel are parameterized to obtain the characteristics of the reduced polarization rotation domain.

[0048] A specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios are selected from the reduced polarization rotation domain features for reduced polarization radar target detection.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0050] The double-channel target backscatter echo vector is obtained according to the reduced polarimetric radar, and the reduced polarimetric covariance matrix is ​​constructed by using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector.

[0051] Perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix;

[0052] Discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence;

[0053] In the direction of the line of sight of the polarized radar, the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix are rotated to obtain a rotated covariance matrix and a rotated coherence matrix;

[0054] The reduced polarization channel coherence pattern and reduced polarization channel correlation pattern are constructed according to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector;

[0055] The coherent pattern of the reduced polarization channel and the correlation pattern of the reduced polarization channel are parameterized to obtain the characteristics of the reduced polarization rotation domain.

[0056] A specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios are selected from the reduced polarization rotation domain features for reduced polarization radar target detection.

[0057] The above-mentioned reduced polarimetric radar polarization rotation domain feature extraction and target detection method, device, computer equipment and storage medium first obtain a dual-channel target backscatter echo vector according to the reduced polarimetric radar, construct a reduced polarimetric covariance matrix using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector, perform pseudo-full polarimetric reconstruction on the reduced polarimetric covariance matrix obtained under specific imaging geometric conditions, rotate the reconstructed pseudo-full polarimetric covariance matrix and pseudo-full polarimetric coherence matrix around the radar line of sight, so as to change the relative geometric relationship between the target attitude and the radar line of sight, and obtain the polarimetric coherence and correlation diagram in the rotation domain by traversing each rotation angle in the rotation domain around the radar line of sight. By parameterizing the polarization coherence and correlation diagram in the rotation domain, we can fully describe the changing characteristics of the target polarization coherence and correlation diagram in the rotation domain, accurately interpret the scattering mechanism of the target in the polarization rotation domain, realize the simplified polarization rotation domain feature extraction and effectively distinguish radar targets, improve the accuracy of radar target recognition, and it is also suitable for various purposes such as earth observation and sea surface monitoring, and has application value in the fields of land object classification and disaster damage assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic flow chart of a method for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar in one embodiment;

[0059] Figure 2 This is a diagram showing the optimization result of simplified polarization rotation domain features in one embodiment;

[0060] Figure 3 A schematic diagram of detection results of Radarsat-2 data based on SO-CFAR in one embodiment;

[0061] Figure 4 It is a schematic diagram of the detection result of GF-3 data based on SO-CFAR in another embodiment;

[0062] Figure 5 A schematic diagram of detection results of Radarsat-2 data based on a superpixel method in one embodiment;

[0063] Figure 6 A schematic diagram of the detection results of GF-3 data based on the superpixel method in one embodiment;

[0064] Figure 7 It is a structural block diagram of a device for extracting polarization rotation domain features and detecting targets of a reduced polarization radar in one embodiment;

[0065] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] In one embodiment, Figure 1 As shown, a method for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar is provided, comprising the following steps:

[0068] Step 102: obtaining a dual-channel target backscatter echo vector according to the reduced polarimetric radar, and constructing a reduced polarimetric covariance matrix using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector.

[0069] Reduced polarimetric radar can obtain dual-channel target backscatter echoes For example, if it is a reduced polarization π / 4 mode or a circular polarization transmission linear polarization reception mode, X and Y represent the H and V reception polarization channels respectively; if it is a dual circular mode, X and Y represent the L and R reception polarization channels respectively. In fact, the flexible combination of the transmission and reception modes of the reduced polarization system can have countless working modes of reduced polarization, including dual polarization working mode.

[0070] The coherence and correlation features between polarization channels are sensitive to the relative geometric relationship between the target attitude and the radar line of sight. The values ​​of the polarization coherence and correlation features of the same target under different attitude conditions may change significantly. This application obtains the target backscatter echo vector of the dual channel based on the reduced polarization radar, and uses the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector to construct a reduced polarization covariance matrix for subsequent feature extraction. This process is a prior art and will not be described in detail in this application.

[0071] Step 104, performing pseudo-full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo-full polarization covariance matrix and a pseudo-full polarization coherence matrix; discretizing the rotation angle in the rotation domain to obtain a discretized rotation angle sequence; rotating the pseudo-full polarization covariance matrix and the pseudo-full polarization coherence matrix in the direction of the polarization radar line of sight to obtain a rotated covariance matrix and a rotated coherence matrix.

[0072] Rotating the reconstructed pseudo full polarization covariance matrix and pseudo full polarization coherence matrix around the radar line of sight can change the relative geometric relationship between the target attitude and the radar line of sight. The rotation process can only be performed after the pseudo full polarization reconstruction of the reduced polarization covariance matrix.

[0073] Step 106: construct a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector.

[0074] The rotation angle θ in the rotation domain is discretized to obtain a discretized rotation angle sequence θ i , The value of N is determined according to the actual situation. By traversing each rotation angle in the rotation domain around the radar line of sight, the reduced polarization channel coherence pattern and reduced polarization channel correlation pattern in the rotation domain can be obtained based on the Pauli vector and the Lexicographic vector. The reduced polarization channel coherence pattern and reduced polarization channel correlation pattern characterize the scattering characteristics of the polarization radar target in the rotation domain around the radar line of sight.

[0075] The Pauli vector is and the lexicographic vector is It can be used to construct four independent simplified polarization channel coherence patterns, namely γ CP(HH+VV)-(HH-VV) (θ), γ CP(HH-VV)-(HV) (θ), γ CP HH-HV (θ) and γ CP HH-VV (θ); Four independent simplified polarization channel correlation patterns, namely and

[0076] Step 108, parameterizing the reduced polarization channel coherence pattern and the reduced polarization channel correlation pattern to obtain reduced polarization rotation domain features.

[0077] By parameterizing the coherence pattern and correlation pattern of the reduced polarization channel in the rotation domain, we can fully describe the changing characteristics of the target polarization coherence and correlation values ​​in the rotation domain, accurately interpret the scattering mechanism of the target in the polarization rotation domain, and then extract the characteristics of the reduced polarization rotation domain.

[0078] Step 110: Select a specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios from the reduced polarization rotation domain features to perform reduced polarization radar target detection.

[0079] The inter-class and intra-class ratios can reflect the potential of polarization features in target detection. The higher the inter-class and intra-class ratios, the greater the degree of inter-class discreteness, the higher the degree of intra-class compactness, and theoretically the better the target detection performance. This application performs an inter-class and intra-class ratio analysis on the derived reduced polarization features based on measured data, and selects the top 12 reduced polarization rotation domain related features with high inter-class and intra-class ratios for reduced polarization radar target detection. The detector can be a traditional CFAR detector, a superpixel-based detector, etc., or a deep learning-based detector.

[0080] In the above-mentioned reduced polarimetric radar polarization rotation domain feature extraction and target detection method, firstly, a dual-channel target backscatter echo vector is obtained according to the reduced polarimetric radar, and a reduced polarimetric covariance matrix is ​​constructed using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector. The reduced polarimetric covariance matrix obtained under specific imaging geometric conditions is pseudo-fully polarimetrically reconstructed, and the reconstructed pseudo-fully polarimetric covariance matrix and pseudo-fully polarimetric coherence matrix are rotated around the radar line of sight, which can change the relative geometric relationship between the target posture and the radar line of sight. By traversing each rotation angle in the rotation domain around the radar line of sight, the polarization coherence and correlation value map in the rotation domain can be obtained. By parameterizing the polarization coherence and correlation diagram in the rotation domain, we can fully describe the changing characteristics of the target polarization coherence and correlation diagram in the rotation domain, accurately interpret the scattering mechanism of the target in the polarization rotation domain, realize the simplified polarization rotation domain feature extraction and effectively distinguish radar targets, improve the accuracy of radar target recognition, and it is also suitable for various purposes such as earth observation and sea surface monitoring, and has application value in the fields of land object classification and disaster damage assessment.

[0081] In one embodiment, performing pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix includes:

[0082] The pseudo full polarization covariance matrix is ​​reconstructed by pseudo full polarization to obtain the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix; the pseudo full polarization covariance matrix is The coherence matrix is

[0083] In one embodiment, the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix are rotated in the line of sight direction of the polarization radar to obtain a rotated covariance matrix and a rotated coherence matrix, including:

[0084] In the direction of the polarization radar line of sight, the pseudo full polarization covariance matrix is ​​rotated to obtain the rotated covariance matrix:

[0085]

[0086] In the direction of the polarization radar line of sight, the pseudo full polarization coherence matrix is ​​rotated to obtain the rotated coherence matrix:

[0087]

[0088] Among them, R 3C (θ) and R 3T (θ) are the rotation matrices of the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix, respectively. T For transposition processing.

[0089] In one embodiment, the Pauli vector is Lexicographic vector

[0090] In one embodiment, constructing a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to a discretized rotation angle sequence, a rotation covariance matrix, a rotation coherence matrix, a Pauli vector, and a Lexicographic vector includes:

[0091] According to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector, four independent reduced polarization channel coherence patterns are constructed:

[0092]

[0093]

[0094]

[0095]

[0096] Among them, <·> is the ensemble average processing, |·| is the absolute value processing, and θ represents the rotation angle.

[0097] In one embodiment, four independent reduced polarization channel correlation patterns are constructed according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector:

[0098]

[0099]

[0100]

[0101]

[0102] In one embodiment, the simplified polarization rotation domain features include original simplified polarization related features, maximum simplified polarization related features, minimum simplified polarization related features, simplified polarization correlation, simplified polarization related fluctuation, simplified polarization related contrast, simplified polarization related anti-entropy, simplified polarization related directional pattern maximization rotation angle, simplified polarization related directional pattern minimization rotation angle and simplified polarization related directional pattern width.

[0103] In a specific embodiment, the reduced polarization rotation domain feature also includes the original reduced polarization coherence feature value: |γ CPX-Y | org =|γ CPX-Y (0)|, the maximum value of the reduced polarization coherence characteristic is: |γ CPX-Y | max =max{|γ CPX-Y (θ)|}, the minimum value of the reduced polarization coherence characteristic is: |γ CPX-Y | min =min{|γ CPX-Y (θ)|}, the reduced polarization coherence is: |γ CPX-Y | mean =mean{|γ CPX-Y (θ)|}, the reduced polarization coherence fluctuation is: |γ CPX-Y | std =std{|γ CPX-Y (θ)|}, the reduced polarization coherence contrast is: |γ CPX-Y | contrast =|γ CPX-Y | max -|γ CPX-Y | min , the simplified polarization coherence anti-entropy is: The maximum rotation angle of the reduced polarization coherence pattern is: θ γCPX-Y-max = argmax{|γ CPX-Y (θ)|}, the minimum rotation angle of the reduced polarization coherence pattern is: θ γCPX-Y-min =arg min{|γ CPX-Y (θ)|}, the width of the reduced polarization coherence pattern is: γ CPbw0.95 =θ 1 -θ 2 ,γ(θ 1 )=γ(θ 2 )=0.95·|γ CPX-Y | max , where max{·} is the maximum value of the sequence; min{·} is the minimum value of the sequence; mean{·} is the mean of the sequence; and std{·} is the standard deviation of the sequence. The same applies below.

[0104] Due to the reflection symmetry condition, a total of 24 independent reduced polarization rotation domain coherent features are obtained. They are |γ CP(HH+VV)-(HH-VV) | max ,|γ CP(HH+VV)-(HV) | max ,|γ CPHH-HV | max ,|γ CPHH-HV | std ,|γ CPHH-HV | mean ,|γ CPHH-VV | org ,|γ CPHH-VV | max ,|γ CPHH-VV | std ,|γ CPHH-VV | mean ,|γ CPHH-VV | min ,|γ CPHH-VV | contrast ,|γ CPHH-VV | A ,θ γCP(HH+VV)-(HH-VV)-max ,θ γCP(HH+VV)-(HV)-max ,θ γCPHH-HV-max ,θ γCPHH-VV-max ,θ γCP(HH+VV)-(HH-VV)-min ,θ γCP(HH+VV)-(HV)-min ,θ γCPHH-HV-min ,θ γCPHH-VV-min , γ CP(HH+VV)-(HH-VV)-bw0.95 , γ CP(HH+VV)-(HV)-bw0.95 , γ CPHH-HV-bw0.95 , and γ CPHH-VV-bw0.95 .

[0105] Similarly, in order to facilitate the use of the reduced polarization correlation pattern, the corresponding characteristic parameters can be similarly defined: the original reduced polarization correlation characteristic Maximum value of reduced polarization-related characteristics Reduced polarization-related characteristic minimum Reduced Polarization Correlation Reduced polarization-dependent fluctuation Reduced polarization-dependent contrast Reduced polarization-related anti-entropy The simplified polarization correlation pattern maximizes the rotation angle, Minimize the rotation angle by reducing the polarization correlation pattern Reduced polarization-dependent pattern width, Due to the reflection symmetry condition, a total of 24 independent reduced polarization rotation domain related features are also obtained.

[0106] In one embodiment, Figure 2 As shown, Figure 2To simplify the polarization rotation domain feature optimization result diagram, the inter-class and intra-class ratios BD / WD between the target and clutter background areas in Radarsat-2 data were statistically analyzed. BD / WD (dB) are all much greater than 0, which indicates that the extracted features have the potential to be used for ship target detection.

[0107] Figure 3 and Figure 4 The detection results of Radarsat-2 data and GF-3 based on SO-CFAR. The input features are the BD / WD (dB) highest reduced polarization rotation domain related features Table 1 gives the quantitative test results. C is the number of ships under inspection, N M is the number of ships missed, N F is the number of false alarms, FoM is the quality factor, defined as The present invention has 1 missed detection and no false alarm in Radarsat-2 data, and the FoM value reaches 97.30%; there are 2 missed detections and 2 false alarms in GF-3 data, and the FoM value reaches 91.30%.

[0108] Table 1

[0109]

[0110] Figure 5 and Figure 6 The detection results of Radarsat-2 data and GF-3 based on superpixel method. The input features are the three reduced polarization rotation domain related features with the highest BD / WD (dB) and The present invention has no missed detection in Radarsat-2 data, has one false alarm, and the FoM value reaches 94.87%, and has no missed detection and no false alarm in GF-3 data, and the FoM value reaches 100%. Table 2 shows the quantitative detection results.

[0111] Table 2

[0112]

[0113] It can be seen that the present application has stable detection results in different data and different detectors, and can be effectively applied to reduced polarimetric radar target recognition.

[0114] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0115] In one embodiment, Figure 7 As shown, a device for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar is provided, comprising: a module 702 for constructing a reduced polarization covariance matrix, a pseudo full polarization reconstruction and rotation module 704, a coherent pattern and a correlation pattern construction module 706, a parameterization characterization module 708 and a target detection module 710, wherein:

[0116] A reduced polarization covariance matrix construction module 702 is used to obtain a dual-channel target backscatter echo vector according to a reduced polarization radar, and to construct a reduced polarization covariance matrix using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector;

[0117] The pseudo full polarization reconstruction and rotation module 704 is used to perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix; discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence; rotate the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix in the direction of the polarization radar line of sight to obtain a rotated covariance matrix and a rotated coherence matrix;

[0118] A coherence pattern and correlation pattern construction module 706 is used to construct a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector;

[0119] A parameterized characterization module 708 is used to parameterize the reduced polarization channel coherence pattern and the reduced polarization channel correlation pattern to obtain reduced polarization rotation domain features;

[0120] The target detection module 710 is used to select a specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios from the reduced polarization rotation domain features to perform reduced polarization radar target detection.

[0121] In one embodiment, the pseudo full polarization reconstruction and rotation module 704 is further used to perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix, including:

[0122] The pseudo full polarization covariance matrix is ​​reconstructed by pseudo full polarization to obtain the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix; the pseudo full polarization covariance matrix is The coherence matrix is

[0123] In one embodiment, the pseudo full polarization reconstruction and rotation module 704 is further used to rotate the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix in the direction of the polarization radar line of sight to obtain a rotated covariance matrix and a rotated coherence matrix, including:

[0124] In the direction of the polarization radar line of sight, the pseudo full polarization covariance matrix is ​​rotated to obtain the rotated covariance matrix:

[0125]

[0126] In the direction of the polarization radar line of sight, the pseudo full polarization coherence matrix is ​​rotated to obtain the rotated coherence matrix:

[0127]

[0128] Among them, R 3C (θ) and R 3T (θ) are the rotation matrices of the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix, respectively. T For transposition processing.

[0129] In one embodiment, in one embodiment, the Pauli vector is Lexicographic vector

[0130] In one embodiment, the coherence pattern and correlation pattern construction module 706 is further used to construct the reduced polarization channel coherence pattern and the reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector, including:

[0131] According to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector, four independent reduced polarization channel coherence patterns are constructed:

[0132]

[0133]

[0134]

[0135]

[0136] Among them, <·> is the ensemble average processing, |·| is the absolute value processing, and θ represents the rotation angle.

[0137] In one embodiment, the coherence pattern and correlation pattern construction module 706 is further used to construct four independent reduced polarization channel correlation patterns according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector:

[0138]

[0139]

[0140]

[0141]

[0142] In one embodiment, the simplified polarization rotation domain features include original simplified polarization related features, maximum simplified polarization related features, minimum simplified polarization related features, simplified polarization correlation, simplified polarization related fluctuation, simplified polarization related contrast, simplified polarization related anti-entropy, simplified polarization related directional pattern maximization rotation angle, simplified polarization related directional pattern minimization rotation angle and simplified polarization related directional pattern width.

[0143] For the specific definition of a reduced polarimetric radar polarization rotation domain feature extraction and target detection device, please refer to the definition of a reduced polarimetric radar polarization rotation domain feature extraction and target detection method above, which will not be repeated here. Each module in the above-mentioned reduced polarimetric radar polarization rotation domain feature extraction and target detection device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0144] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for extracting features and detecting targets in the polarization rotation domain of a reduced polarization radar is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0145] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0147] In one embodiment, a computer storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0148] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0149] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar, characterized in that: The method comprises: Obtaining a dual-channel target backscatter echo vector according to a reduced polarimetric radar, and constructing a reduced polarimetric covariance matrix using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector; Performing pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix; Discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence; Rotating the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix in the direction of the line of sight of the polarization radar to obtain a rotated covariance matrix and a rotated coherence matrix; Constructing a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector; Parameterizing the coherent directional pattern of the reduced polarization channel and the correlation directional pattern of the reduced polarization channel to obtain the characteristics of the reduced polarization rotation domain; A specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios are selected from the reduced polarization rotation domain features to perform reduced polarization radar target detection.

2. The method according to claim 1, characterized in that: Performing pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization covariance matrix, including: The simplified polarization covariance matrix is ​​reconstructed in pseudo full polarization to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix; the pseudo full polarization covariance matrix is The pseudo full polarization coherence matrix is 3. The method according to claim 1, characterized in that In the direction of the line of sight of the polarized radar, the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix are rotated to obtain a rotated covariance matrix and a rotated coherence matrix, including: In the direction of the polarization radar line of sight, the pseudo full polarization covariance matrix is ​​rotated to obtain a rotated covariance matrix: In the direction of the polarization radar line of sight, the pseudo full polarization coherence matrix is ​​rotated to obtain a rotated coherence matrix: Among them, R 3C (θ) and R 3T (θ) are the rotation matrices of the pseudo full polarization covariance matrix and pseudo full polarization coherence matrix, respectively. T For transposition processing.

4. The method according to claim 3, characterized in that The Pauli vector is The Lexicographic Vector 5. The method according to claim 4, characterized in that Constructing a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector, including: Four independent reduced polarization channel coherence patterns are constructed according to the discretized rotation angle sequence, rotation covariance matrix, rotation coherence matrix, Pauli vector and Lexicographic vector: Among them, <·> is the ensemble average processing, · is the absolute value processing, and θ represents the rotation angle.

6. The method according to claim 5, characterized in that The method further comprises: constructing four independent reduced polarization channel correlation directional diagrams according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector:

7. The method according to claim 6, characterized in that The simplified polarization rotation domain features include original simplified polarization related features, maximum simplified polarization related features, minimum simplified polarization related features, simplified polarization correlation, simplified polarization related fluctuation, simplified polarization related contrast, simplified polarization related anti-entropy, simplified polarization related directional pattern maximization rotation angle, simplified polarization related directional pattern minimization rotation angle and simplified polarization related directional pattern width.

8. A device for extracting features and detecting targets in polarization rotation domain of a reduced polarization radar, characterized in that: The device comprises: Constructing a reduced polarization covariance matrix module, which is used to obtain a dual-channel target backscatter echo vector according to a reduced polarization radar, and constructing a reduced polarization covariance matrix using the target backscatter echo vector and the conjugate transpose of the target backscatter echo vector; The pseudo full polarization reconstruction and rotation module is used to perform pseudo full polarization reconstruction on the reduced polarization covariance matrix to obtain a pseudo full polarization covariance matrix and a pseudo full polarization coherence matrix; discretize the rotation angle in the rotation domain to obtain a discretized rotation angle sequence; rotate the pseudo full polarization covariance matrix and the pseudo full polarization coherence matrix in the direction of the polarization radar line of sight to obtain a rotated covariance matrix and a rotated coherence matrix; A coherence pattern and correlation pattern construction module, used to construct a reduced polarization channel coherence pattern and a reduced polarization channel correlation pattern according to the discretized rotation angle sequence, the rotation covariance matrix, the rotation coherence matrix, the Pauli vector and the Lexicographic vector; A parameterized characterization module, used for parameterizing the coherent directional pattern of the reduced polarization channel and the related directional pattern of the reduced polarization channel to obtain the characteristics of the reduced polarization rotation domain; The target detection module is used to select a specific number of reduced polarization rotation domain related features with high inter-class and intra-class ratios from the reduced polarization rotation domain features to perform reduced polarization radar target detection.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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