A scanning radar target detection method based on graph data and graph embedding

Abstract

The present invention relates to a scanning radar target detection method based on graph data and graph embedding, and belongs to the field of radar signal processing. To address the problem of underutilization of temporal and spatial relationship information between signal samples in existing methods, resulting in limited performance, the scanning radar target detection method based on graph data and graph embedding includes eight steps: acquiring scanning radar echo data; performing over-threshold detection; acquiring a detection sample set; setting azimuth correlation thresholds and distance correlation thresholds to obtain radar signal graph data; model training; obtaining an embedding vector; and displaying the results. This method fully utilizes the temporal and spatial correlation information between the target and false alarm points, improving target detection performance in complex environments and enhancing detection accuracy.

Description

Technical Field

[0001] The present invention relates to a scanning radar target detection method based on graph data and graph embedding, and belongs to the field of radar signal processing. Background Art

[0002] Target detection has widespread applications in both military and civilian fields. Radar is a key means of target detection and surveillance. However, due to the clutter generated by complex environments and the diverse types of targets, reliable and robust target detection remains a key technology in need of research. In recent years, graph theory has rapidly developed in the field of signal processing. Representing signals using graphs preserves signal characteristics and also has the ability to represent data associations, providing new approaches to signal processing. In the field of radar signal processing, graph-theoretic methods have been applied to processing signals such as SAR images.

[0003] Previous researchers have attempted to use various convolutional neural networks to process radar signal time series, distinguishing targets from clutter signals through binary classification to achieve target detection. These methods rely solely on signal sequence characteristics for classification. During training and testing, each signal sample is completely independent of each other, and the temporal and spatial relationship information between signal samples is not fully utilized, limiting performance. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem mentioned in the above background technology that the time domain and spatial domain relationship information between each signal sample in the existing method is not fully utilized and the performance is limited, and to provide a detection method with high radar template detection accuracy. The method is based on graph data and graph embedding to scan radar targets.

[0005] To solve the above problems, the scanning radar target detection method based on graph data and graph embedding of the present invention is implemented through the following technical solutions:

[0006] A scanning radar target detection method based on graph data and graph embedding is special in that it includes the following steps:

[0007] Step 100: Acquire a scanning radar echo signal;

[0008] Using continuous multi-frame clutter and target radar echo signals, signal S∈C AZ×R×L , AZ is the number of azimuth units, R is the number of range units, L is the number of scanning circles, and C is the complex domain symbol;

[0009] Step 200: Threshold crossing detection: performing threshold crossing detection on each scanning cycle of the radar echo signal. A detection threshold is independently set for the signal of each scanning cycle to obtain multiple binary detection result graphs. Each scanning cycle signal corresponds to a binary detection result graph. The value of the distance-angle unit that crosses the threshold is 1, and the corresponding value of other units is 0.

[0010] S' ijk ∈{0,1}

[0011] Where S' is the binary detection result image,

[0012] S' is a three-dimensional data, i, j and k are indexes, corresponding to the orientation unit number, distance unit number and scanning frame number of each data;

[0013] Step 300: Obtain a test sample set: Perform 4 or 8 connected domain detection on each binary test result graph to obtain t connected domains.

[0014] t i Indicates the number of connected domains in the binary detection result graph corresponding to the i-th scanning cycle signal;

[0015] Number each connected domain, and count the scanning cycle number corresponding to the binary detection result graph of each connected domain as the frame number of the connected domain. i ; The number of angle-distance units contained in each connected domain is taken as the area c of the connected domain i ;

[0016] Calculate the average orientation, average distance, frame number and number of angle-distance units contained in each connected domain to obtain the detection sample set;

[0017] Calculate the average orientation az of each connected region i : Extract the orientation unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average orientation of the connected domain.

[0018]

[0019] Calculate the average observation distance r of each connected region i : Extract the distance unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average observation distance of the connected domain;

[0020] Get the detection sample set V = {az, r, l, c};

[0021] Each connected domain corresponds to a target / false alarm sample, corresponding to a node, and the node feature is the average orientation az of the corresponding connected domain. i, average distance r i , frame number l i and area c i ;

[0022] Step 400: Setting the position correlation threshold thr az And the distance association threshold thr range ;

[0023] thr az And the distance association threshold thr range It is an adjustable variable, which is adjusted according to information such as the application scenario; Step 500: Obtain radar signal map data:

[0024] According to the orientation correlation threshold thr az , distance association threshold thr range , the feature T of each node i , constructing an adjacency matrix to obtain radar signal graph data; the graph data includes the adjacency matrix and node features of each node;

[0025] Specifically, the weight of the edge between any two nodes is determined using the following formula:

[0026]

[0027] Where V i,2 is the average orientation of the i-th node, V j,2 is the average orientation of the jth node; V i,3 is the average distance of the i-th node, V j,3 is the average distance of the jth node; V i,4 is the frame number of the i-th node, V j,4 is the frame number of the jth node; thr az and thr range is the preset variable;

[0028] Then, based on the edge weights between all nodes, each node is numbered sequentially and an adjacency matrix is ​​constructed. For example, the adjacency matrix A ij is the edge weight between the i-th node and the j-th node. Finally, according to the adjacency matrix and the node characteristics of each node, the signal graph data can be obtained;

[0029] Step 600: Model training:

[0030] The signal graph data is sampled by random walk; the sampling sequence is input into the skip-gram model to train the model;

[0031] The sampling sequence is obtained using the following formula:

[0032]

[0033] Where d(ν (t) ) is the degree of the node;

[0034] Step 700: Get embedding vector:

[0035] Repeat step 600, sample the signal graph data through random walk, input the sample sequence into the trained skip-gram model, and obtain the embedding vector F = {f1, f2, ..., f t};

[0036] f i is the embedding vector corresponding to the i-th node;

[0037] Step 800: Result display:

[0038] The embedding vector of each node is input into the classifier, and the classification result of each node is output; the classification result of each node is a target or a false alarm.

[0039] This method makes full use of the temporal and spatial correlation information between the target and the false alarm points, improves the target detection performance in complex environments, and improves the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] For ease of explanation, the present invention is described in detail with reference to the following specific implementations and accompanying drawings.

[0041] Figure 1 : Algorithm flow chart of this application;

[0042] Figure 2 : Variable flow chart of this application; DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1. Figure 1-2 The scanning radar target detection method based on graph data and graph embedding shown in FIG comprises the following steps:

[0045] Step 100: Acquire a scanning radar echo signal;

[0046] The radar echo signal in this embodiment is the echo data after pulse compression; the echo signal is obtained by a scanning radar, and the azimuth-range-scanning frame three-dimensional data is obtained after pulse compression;

[0047] Using continuous multi-frame clutter and target radar echo signals, signal S∈C AZ×R×L , AZ is the number of azimuth units, R is the number of range units, L is the number of scanning circles, and C is the complex domain symbol;

[0048] Step 200: Threshold crossing detection: performing threshold crossing detection on each scanning cycle of the radar echo signal. A detection threshold is independently set for the signal of each scanning cycle to obtain multiple binary detection result graphs. Each scanning cycle signal corresponds to a binary detection result graph. The value of the distance-angle unit that crosses the threshold is 1, and the corresponding value of other units is 0.

[0049] S' ijk ∈{0,1}

[0050] Where S' is the binary detection result image,

[0051] S' is a three-dimensional data, i, j and k are indexes, corresponding to the orientation unit number, distance unit number and scanning frame number of each data;

[0052] Step 300: Obtain the detection sample set: perform 8-connected domain detection on each binary detection result graph to obtain t connected domains.

[0053] t i Indicates the number of connected domains in the binary detection result graph corresponding to the i-th scanning cycle signal;

[0054] Number each connected domain, and count the scanning cycle number corresponding to the binary detection result graph of each connected domain as the frame number of the connected domain. i ; The number of angle-distance units contained in each connected domain is taken as the area c of the connected domain i ;

[0055] Calculate the average orientation, average distance, frame number and number of angle-distance units contained in each connected domain to obtain the detection sample set;

[0056] Calculate the average orientation az of each connected region i : Extract the orientation unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average orientation of the connected domain.

[0057] Calculate the average observation distance r of each connected region i : Extract the distance unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average observation distance of the connected domain;

[0058] Get the detection sample set V = {az, r, l, c};

[0059] Each connected domain corresponds to a target / false alarm sample, corresponding to a node, and the node feature is the average orientation az of the corresponding connected domain. i , average distance r i , frame number l i and area c i ;

[0060] Step 400: Setting the position correlation threshold thr az And the distance association threshold thr range ;

[0061] thr az And the distance association threshold thr range It is an adjustable variable that is adjusted according to information such as the application scenario;

[0062] Step 500: Obtain radar signal image data:

[0063] According to the orientation correlation threshold thr az , distance association threshold thr range , the feature T of each node i , constructing an adjacency matrix to obtain radar signal graph data; the graph data includes the adjacency matrix and node features of each node;

[0064] Specifically, the weight of the edge between any two nodes is determined using the following formula:

[0065]

[0066] Where V i,2 is the average orientation of the i-th node, V j,2 is the average orientation of the jth node; V i,3 is the average distance of the i-th node, V j,3 is the average distance of the jth node; V i,4 is the frame number of the i-th node, V j,4 is the frame number of the jth node; thr az and thr range is the preset variable;

[0067] Then, based on the edge weights between all nodes, each node is numbered sequentially and an adjacency matrix is ​​constructed. For example, the adjacency matrix A ij is the edge weight between the i-th node and the j-th node. Finally, according to the adjacency matrix and the node characteristics of each node, the signal graph data can be obtained;

[0068] Step 600: Model training:

[0069] The signal graph data is sampled by random walk; the sampling sequence is input into the skip-gram model to train the model;

[0070] The sampling sequence is obtained using the following formula:

[0071]

[0072] Where d(ν (t) ) is the degree of the node; Step 700: Get the embedding vector:

[0073] Repeat step 600, sample the signal graph data through random walk, input the sample sequence into the trained skip-gram model, and obtain the embedding vector F of each node = {f1, f2, ..., f t};

[0074] f i is the embedding vector corresponding to the i-th node;

[0075] Step 800: Result display:

[0076] The embedding vector of each node is input into the classifier, and the classification result of each node is output; the classification result of each node is a target or a false alarm.

[0077] Example 2. This example is a scanning radar target detection method based on graph data and graph embedding. The difference is that in step 300, a 4-connected domain detection is performed on each binary detection result graph to obtain t connected domains.

[0078] t i Indicates the number of connected domains in the binary detection result graph corresponding to the i-th scanning cycle signal;

[0079] Number each connected domain, and count the scanning cycle number corresponding to the binary detection result graph of each connected domain as the frame number of the connected domain. i ; The number of angle-distance units contained in each connected domain is taken as the area c of the connected domain i ;

[0080] Calculate the average orientation, average distance, frame number and number of angle-distance units contained in each connected domain to obtain the detection sample set;

[0081] Calculate the average orientation az of each connected region i : Extract the orientation unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average orientation of the connected domain.

[0082]

[0083] Calculate the average observation distance r of each connected region i : Extract the distance unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average observation distance of the connected domain;

[0084]

[0085] Get the detection sample set V = {az, r, l, c};

[0086] Each connected domain corresponds to a target / false alarm sample, corresponding to a node, and the node feature is the average orientation az of the corresponding connected domain. i , average distance r i , frame number l i and area c i .

Claims

1. A scanning radar target detection method based on graph data and graph embedding, characterized by: The following steps are involved: Step 100: Acquire a scanning radar echo signal; Step 200: Threshold crossing detection: performing threshold crossing detection on each scanning cycle of the radar echo signal, independently setting a detection threshold for the signal of each scanning cycle, and obtaining a plurality of binary detection result images; Step 300: Obtain the detection sample set: perform connected domain detection on each binary detection result graph, number each connected domain, Calculate the average orientation, average distance, frame number and number of angle-distance units contained in each connected domain to obtain the detection sample set; Step 400: Setting the position correlation threshold thr az And the distance association threshold thr range ; Step 500: Obtain radar signal image data: According to the orientation correlation threshold thr az , distance association threshold thr range , the feature T of each node i , construct the adjacency matrix and obtain the radar signal graph data; Step 600: Model training: The signal graph data is sampled by random walk; the sampling sequence is input into the skip-gram model to train the model; Step 700: Get embedding vector: Repeat step 600, sample the signal graph data through random walk, input the sample sequence into the trained skip-gram model, and obtain the embedding vector F of each node = {f1, f2, ..., f t }; f i is the embedding vector corresponding to the i-th node; Step 800: Result display: The embedding vector of each node is input into the classifier, and the classification result of each node is output; the classification result of each node is a target or a false alarm.

2. The scanning radar target detection method based on graph data and graph embedding according to claim 1, characterized in that: In step 100, continuous multi-frame clutter and target radar echo signals are used, and the signal S∈C AZ×R×L , AZ is the number of azimuth units, R is the number of distance units, and L is the number of scanning circles.

3. The scanning radar target detection method based on graph data and graph embedding according to claim 1 or 2, characterized in that: In step 200: each scanning cycle signal corresponds to a binary detection result image, the distance-angle unit value that exceeds the threshold is 1, and the corresponding value of other units is 0; S’ ijk ∈{0,1}。 4. The scanning radar target detection method based on graph data and graph embedding according to claim 3, characterized in that: In step 300, a 4- or 8-connected domain detection is performed on each binary detection result graph to obtain t connected domains. t i Indicates the number of connected domains in the binary detection result graph corresponding to the i-th scanning cycle signal; Number each connected domain, and count the scanning cycle number corresponding to the binary detection result graph of each connected domain as the frame number of the connected domain. i ; The number of angle-distance units contained in each connected domain is taken as the area c of the connected domain i .

5. The scanning radar target detection method based on graph data and graph embedding according to claim 4, characterized in that: In step 300: Calculate the average orientation az of each connected region i : Extract the orientation unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average orientation of the connected domain; 6. The scanning radar target detection method based on graph data and graph embedding according to claim 4, characterized in that: In step 300: Calculate the average observation distance r of each connected region i : Extract the distance unit numbers of all angle-distance units in each connected domain and calculate the weighted average as the average observation distance of the connected domain; The detection sample set V = {az, r, l, c} is obtained.

7. The scanning radar target detection method based on graph data and graph embedding according to claim 4, characterized in that: In step 300: Each connected domain corresponds to a target / false alarm sample, corresponding to a node, and the node feature is the average orientation az of the corresponding connected domain. i , average distance r i , frame number l i and area c i .

8. The scanning radar target detection method based on graph data and graph embedding according to claim 4, characterized in that: In step 500: The graph data includes an adjacency matrix and node features of each node; Specifically, the weight of the edge between any two nodes is determined using the following formula: Where V i,2 is the average orientation of the i-th node, V j,2 is the average orientation of the jth node; V i,3 is the average distance of the i-th node, V j,3 is the average distance of the jth node; V i,4 is the frame number of the i-th node, V j,4 is the frame number of the jth node; thr az and thr range is the preset variable; Then, based on the edge weights between all nodes, each node is numbered sequentially and an adjacency matrix is ​​constructed. For example, the adjacency matrix A ij is the edge weight between the i-th node and the j-th node; finally, according to the adjacency matrix and the node characteristics of each node, the signal graph data can be obtained.

9. The scanning radar target detection method based on graph data and graph embedding according to claim 7, characterized in that: In step 600: the sampling sequence is obtained using the following formula: Where d(ν (t) ) is the degree of the node.

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