Radar active interference suppression method, system, medium and device

Abstract

The present invention discloses a radar active interference suppression method, system, medium, and device, comprising: constructing a training set and a test set; constructing a self-supervised training ViT network; using the training set to train the self-supervised training ViT network to obtain a trained self-supervised training ViT network; using the trained self-supervised training ViT network to extract interference features from a time-frequency graph in the test set to obtain a self-supervised interference feature graph; performing interference location processing on the self-supervised interference feature graph to obtain a self-supervised interference feature graph with an interference location frame; and performing interference suppression on the time-frequency graph based on the self-supervised interference feature graph with the interference location frame. The present invention can eliminate the reliance on manual data labeling during CNN network training and avoid the drawback of CNN focusing only on local information, thereby improving the generalization of the network and the accuracy of interference location, thereby improving the interference suppression effect.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal interference suppression, and in particular to a radar active interference suppression method, system, medium and equipment. Background Art

[0002] Currently, there are many methods for countering radar intermittent sampling and forwarding interference, such as frequency-agile waveform and mismatch filter design, interference reconstruction and cancellation, and bandpass filter design. Most of these methods require parameter estimation of the interfering signal, so anti-interference performance depends on the accuracy of this parameter estimation. However, in complex interference scenarios, parameter estimation of the interfering signal is more difficult and less accurate than for a single interference signal, resulting in poor interference suppression.

[0003] In recent years, some CNN networks have been proposed for detecting and locating interference in radar signal time-frequency graphs, and have achieved some impressive results. However, most CNN networks still have the following shortcomings:

[0004] First, a large amount of manually labeled radar data is required for training, but real-world radar data is difficult to obtain, and labeling also requires a lot of manual work;

[0005] Second, the CNN network only considers local information, which has a certain bias when processing data and has a certain impact on the final result;

[0006] Third, CNN networks are generally unable to process data types that have not been seen during training, have difficulty coping with scenarios where complex interference methods change rapidly, and have poor generalization. Summary of the Invention

[0007] In order to solve the technical problems existing in the background technology, the present invention proposes a radar active interference suppression method, system, medium and equipment.

[0008] The present invention proposes a radar active interference suppression method based on self-supervised training ViT network, comprising:

[0009] Obtain radar active interference time-frequency atlas without interference type labels;

[0010] Extract radar active interference time-frequency maps from the radar active interference time-frequency map set without interference type labels, and combine them with the ImageNet dataset to form a training set;

[0011] The remaining radar active interference time-frequency graphs in the radar active interference time-frequency graph set without interference type labels are combined into a test set;

[0012] Construct a self-supervised training ViT network;

[0013] Use the training set to train the self-supervised training ViT network to obtain a trained self-supervised training ViT network;

[0014] The trained self-supervised ViT network is used to extract interference features from the time-frequency graph in the test set to obtain a self-supervised interference feature graph;

[0015] Perform interference location processing on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference location frame;

[0016] Interference suppression is performed on the time-frequency map based on the self-supervised interference feature map with interference localization boxes.

[0017] Preferably, the self-supervised training ViT network includes a teacher network and a student network, and both the teacher network and the student network use the VIT-tiny network model as a basic model.

[0018] Preferably, the VIT-tiny network model includes: an image segmentation module, a patch encoding module, and a transformer encoder connected in sequence;

[0019] Among them, the transformer encoder includes 6 transformer layers connected in sequence; among them, each transformer layer includes a first normalization layer, a multi-head self-attention layer, a second normalization layer and a multi-layer perceptron, the input end of the first normalization layer serves as the input end of the transformer layer, the output end of the first normalization layer is connected to the input end of the multi-head self-attention layer, and the input end of the first normalization layer and the output end of the multi-head self-attention layer are added and connected to the input end of the second normalization layer, the output end of the second normalization layer is connected to the input end of the multi-layer perceptron, and the input end of the second normalization layer and the output end of the multi-layer perceptron are added and connected as the output end of the transformer layer.

[0020] Preferably, a focused linear attention module is used to replace the self-attention module in the softmax function of the VIT-tiny network.

[0021] Preferably, the interference feature extraction of the time-frequency graph in the test set is performed using the trained self-supervised training ViT network to obtain a self-supervised interference feature graph, specifically including:

[0022] The time-frequency graphs in the test set are subjected to two different random transformations;

[0023] The two different randomly transformed time-frequency graphs are input into the teacher network and student network of the self-supervised training ViT network respectively to obtain two output features;

[0024] The two output features are normalized using the softmax function to obtain the self-supervised interference features.

[0025] Preferably, during the training process of the self-supervised training ViT network, the loss function is loss = -P2logP1, where P1 is the output of the teacher network, P2 is the output of the student network, and the stop gradient operator is applied to the teacher network, and the gradient is only propagated through the student network. The teacher parameters are updated using the exponential moving average of the student network parameters; the optimizer uses AdamW, and the learning rate uses cosine scheduling to decay the learning rate.

[0026] Preferably, interference suppression is performed on the time-frequency graph according to the self-supervised interference feature graph with the interference positioning frame, specifically including:

[0027] According to the self-supervised interference feature map with the interference positioning frame, the coordinates of the interference positioning frame are obtained;

[0028] According to the coordinates of the interference positioning frame, the self-supervised interference feature map with the interference positioning frame is segmented to obtain the interference segmentation result map;

[0029] According to the interference segmentation result map, an interference mask map corresponding to the interference in the time-frequency map is obtained;

[0030] According to the interference mask, the interference is filtered out from the time-frequency diagram.

[0031] The present invention also proposes a radar active interference suppression system based on self-supervised training ViT network, comprising:

[0032] An acquisition module, used to obtain a radar active interference time-frequency atlas without interference type labels;

[0033] The training set construction module is used to extract radar active interference time-frequency maps from the radar active interference time-frequency map set without interference type labels and combine it with the ImageNet dataset to form a training set;

[0034] A test set construction module is used to form a test set from the remaining radar active interference time-frequency graphs in the radar active interference time-frequency graph set without interference type labels;

[0035] Model building module, used to build self-supervised training ViT network;

[0036] A training module is used to train the self-supervised training ViT network using the training set to obtain a trained self-supervised training ViT network;

[0037] The testing module is used to extract interference features from the time-frequency graph in the test set using the trained self-supervised training ViT network to obtain a self-supervised interference feature graph;

[0038] A positioning module is used to perform interference positioning processing on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference positioning frame;

[0039] The suppression module is used to suppress interference on the time-frequency map based on the self-supervised interference feature map with the interference localization box.

[0040] The present invention also proposes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the radar active interference suppression method based on the self-supervised training ViT network as described in any one of the above is implemented.

[0041] The present invention also proposes an electronic device comprising: a processor and a memory, the memory being used to store one or more programs. When the one or more programs are executed by the processor, the radar active interference suppression method based on the self-supervised training ViT network as described in any one of the above is implemented.

[0042] In the present invention, the proposed radar active interference suppression method, system, medium and device adopt a self-supervised ViT network to extract interference features, make full use of radar active interference time-frequency maps without interference type labels in a deep comparative learning manner, conduct self-supervised comparative learning on the radar active interference time-frequency maps without interference type labels and the training set composed of the ImageNet data set, effectively extract self-supervised interference feature maps, eliminate the dependence on manual data labeling during CNN network training and avoid the drawback of CNN only focusing on local information, improve the generalization of the network and the accuracy of interference positioning, thereby improving the effect of interference suppression BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a flow chart of a radar active interference suppression method based on self-supervised training of a ViT network in one embodiment of the present invention.

[0044] Figure 2 This is a block diagram of a radar active interference suppression system based on self-supervised training ViT network in one embodiment of the present invention.

[0045] Figure 3 FIG. 1 is a schematic diagram of interference according to an embodiment of the present invention.

[0046] Figure 4 A comparison chart of the results of feature extraction of radar active jammers using different networks.

[0047] Figure 5 The interference location comparison chart shows the results of feature extraction of radar active interference using different networks.

[0048] Figure 6The figure shows the time-frequency comparison results after using different networks to suppress radar active interference.

[0049] Figure 7 This is the pulse compression result after using different networks to suppress radar active interference. DETAILED DESCRIPTION

[0050] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] Reference Figure 1 The present invention proposes a radar active interference suppression method based on self-supervised training ViT network, comprising:

[0052] Obtain radar active interference time-frequency atlas without interference type labels;

[0053] Extract radar active interference time-frequency maps from the radar active interference time-frequency map set without interference type labels, and combine them with the ImageNet dataset to form a training set;

[0054] The remaining radar active interference time-frequency graphs in the radar active interference time-frequency graph set without interference type labels are combined into a test set;

[0055] Construct a self-supervised training ViT network;

[0056] Use the training set to train the self-supervised training ViT network to obtain a trained self-supervised training ViT network;

[0057] The trained self-supervised ViT network is used to extract interference features from the time-frequency graph in the test set to obtain a self-supervised interference feature graph;

[0058] Perform interference location processing on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference location frame;

[0059] Interference suppression is performed on the time-frequency map based on the self-supervised interference feature map with interference localization boxes.

[0060] Compared with the traditional CNN network, the present invention adopts a self-supervised ViT network for interference feature extraction, fully utilizes the radar active interference time-frequency diagram without interference type labels in a deep contrastive learning manner, and performs self-supervised contrastive learning on the training set consisting of the radar active interference time-frequency diagram without interference type labels and the ImageNet dataset, effectively extracting self-supervised interference feature maps, eliminating the dependence on manual data labeling during CNN network training and avoiding the drawback of CNN only focusing on local information, improving the generalization of the network and the accuracy of interference positioning, thereby improving the effect of interference suppression.

[0061] In this embodiment, obtaining a radar active interference time-frequency atlas without an interference type label specifically includes:

[0062] Obtain radar active jammer dataset without jammer type labels;

[0063] The radar active interference dataset without interference type labels is subjected to time-frequency processing to obtain a radar active interference time-frequency atlas without interference type labels.

[0064] Among them, 80% of the radar active jamming time-frequency atlas collection is mixed with the ImageNet dataset as the training set, and the remaining 20% ​​of the radar active jamming time-frequency atlas collection is used as the test set.

[0065] In this embodiment, obtaining a radar active interference dataset without an interference type label specifically includes:

[0066] Use matlab to simulate the signal and get the radar echo signal;

[0067] Radar echo signals are combined into a radar active interference time-frequency atlas without interference type labels.

[0068] Among them, MATLAB is used to perform signal simulation to obtain radar echo signals, including:

[0069] Set the radar signal waveform; the radar signal waveform is a linear frequency modulation (LFM) signal, and its time domain expression is as follows:

[0070]

[0071] Where A is the signal amplitude, f0 is the frequency, T and B are the time width and bandwidth respectively; k is the frequency modulation slope, k = T / B; is a rectangular function,

[0072] In one specific embodiment, f0 is set to 3×10 7 Hz, T=20×10 6 s, B = 10 6 Hz. At the same time, considering the long range of the radar, the target is set as a point target in the simulation.

[0073] In this embodiment, time-frequency processing is performed on the radar active interference time-frequency atlas without the interference type label to obtain the radar active interference time-frequency atlas without the interference type label, specifically including:

[0074] Short-time Fourier transform (STFT) is used to perform time-frequency processing on the radar active interference time-frequency atlas without interference type labels to obtain the radar active interference time-frequency atlas without interference type labels.

[0075] Among them, short-time Fourier transform (STFT) is a time-frequency analysis method that uses a window function to segment the signal and performs Fourier transform to convert the one-dimensional time domain data into two-dimensional time-frequency domain data. The drawing function is used to convert the two-dimensional time-frequency domain data into interference time-frequency graph data.

[0076] In this embodiment, the window function uses a Hamming window, the sampling point is 1024, and the expression of the short-time Fourier transform is:

[0077]

[0078] Where τ is φ * The center position of (τ-t), * indicates the conjugation operation.

[0079] The interference types include single interference signals of different categories and composite slice interference signals of different slice combinations.

[0080] like Figure 3 As shown, in one specific embodiment, the interference signal types include five single interferences and four composite interferences. Among them, the five single interference signals are Interrupted Sampling and Repeater Jamming With Overlay Forwarding (ISRJOF), Interrupted-sampling Frequency Shift Repeater jamming (ISFSRJ), Mono-Pulse Dense Forwarding Jamming (MDFJ), Non-uniform Interrupted Sampling Repeater Jamming (NUISRJ), and Modulated Interrupt Sampling Repeater Jamming (MISRJ).

[0081] It is important to know that ISRJOF Figure 3 As shown in (a), ISFSRJ Figure 3 As shown in (b), MDFJ Figure 3 As shown in (c), NUISRJ Figure 3 As shown in (d), MISRJ Figure 3(e) shown.

[0082] Among them, the four composite interferences in this embodiment are ISFSRJ and NUISRJ composite interference, MDFJ and NUISRJ composite interference, ISFSRJ and MISRJ composite interference, and ISRJOF and NUISRJ composite interference, as shown respectively. Figure 3 (f) Figure 3 (g) Figure 3 (h) and Figure 3 (i) shown.

[0083] The ISRJOF works by repeatedly forwarding a radar signal after intercepting it. As the number of repeated forwardings increases, an interference signal may appear in the next PRT. The signal time domain model is as follows:

[0084]

[0085] S * =S(t-(mτ+(m-1)T s )+(M+1)(n-1)T s -L Jam );

[0086] L Jam =N(M+1)T w ;

[0087] Where N is the number of slices, T s is the slice width, M is the number of forwarding times, τ r is the jammer sampling time, and S(t) is the radar transmission signal.

[0088] ISFSRJ uses a segmented frequency shift method for sampling signals whose frequency components are at the edge of the bandwidth within the sampling period. Its signal time domain model can be expressed as:

[0089]

[0090] Where, f j Indicates frequency shift, which will produce lead or lag interference. j >0 forms leading interference, when f j When <0, hysteresis interference is formed.

[0091] MDFJ samples only a portion of the radar signal and forwards it multiple times, forming a dense group of false targets. The signal time domain model is as follows:

[0092]

[0093] NUISRJ samples the received signal at non-uniform time intervals and then forwards these sampled values ​​to other locations. The signal time domain model is as follows:

[0094]

[0095] Where, T n =(M+1)T s represents the period of each sampling pulse, Indicates the time when the nth sampling pulse starts, τ n Indicates delay.

[0096] MISRJ first converts the receiving antenna into a received radar signal, which is then bandpass filtered, down-converted, and converted to a digital signal. The sampled portion of the transmitted signal is then modulated with a pre-set filter to produce a modulated interference signal. The signal's time domain model is as follows:

[0097] S s =p(t)*S(t);

[0098]

[0099] S MISRJ (t) = IFT(H(j)*S s (f));

[0100] Where p(t) represents the sampling function, T c represents the interference patch width, τ c Indicates delay, S s (f) and IFT(·) represent S s (t) Fourier transform and inverse Fourier transform operations.

[0101] In this embodiment, after forming the training set and the test set, data enhancement is performed on the time-frequency graphs in the training set and the test set, respectively. The data enhancement includes color jittering, Gaussian blurring, and multiple cropping.

[0102] In this embodiment, the self-supervised training ViT network includes a teacher network and a student network, and both the teacher network and the student network use the VIT-tiny network model as the basic model.

[0103] In a further embodiment, the VIT-tiny network model includes: an image segmentation module, a patch encoding module, and a transformer encoder connected in sequence;

[0104] Among them, the transformer encoder includes 6 transformer layers connected in sequence; among them, each transformer layer includes a first normalization layer, a multi-head self-attention layer, a second normalization layer and a multi-layer perceptron, the input end of the first normalization layer serves as the input end of the transformer layer, the output end of the first normalization layer is connected to the input end of the multi-head self-attention layer, and the input end of the first normalization layer and the output end of the multi-head self-attention layer are added and connected to the input end of the second normalization layer, the output end of the second normalization layer is connected to the input end of the multi-layer perceptron, and the input end of the second normalization layer and the output end of the multi-layer perceptron are added and connected as the output end of the transformer layer.

[0105] In this embodiment, the self-supervised training ViT network using such a structure can improve the accuracy of the interference features in the extracted self-supervised interference feature map, which is beneficial to the subsequent detection, positioning and segmentation of interference, and thus beneficial to the subsequent interference suppression effect.

[0106] Specifically, the input of each transformer layer first passes through the first normalization layer, then through the multi-head self-attention layer to obtain multi-head self-attention relative to itself. The input of the first normalization layer and the output of the multi-head self-attention layer are fused using a residual structure and passed through the second normalization layer. The resulting data is then sent to a multilayer perceptron (MLP). The MLP contains two linear layers, a fully connected layer, and two random dropout layers to prevent overfitting during training.

[0107] The image segmentation module is used to divide the input image into image blocks of a patch size to better extract detailed features. In this embodiment, the input image size is 240×240 (pixels), the patch size is a square with a side length of 6 pixels, and the number of patches is 1600.

[0108] Among them, the patch encoding module is used to encode image blocks into Patch Embedding, which is integrated with the learnable classification token and position encoding and then input into the transformer encoder; the transformer encoder is used to obtain a feature vector representing the entire image based on the integrated learnable classification token and position encoding, including the global meaning and information in the context of the entire image.

[0109] In a further embodiment, a focused linear attention module is used to replace the self-attention module in the softmax function of the VIT-tiny network to change the attention calculation method, improve the efficiency of the self-attention calculation, and thus optimize the efficiency of the VIT-tiny network model.

[0110] In a further embodiment, the interference feature extraction of the time-frequency graph in the test set is performed using the trained self-supervised training ViT network to obtain a self-supervised interference feature graph, specifically including:

[0111] The time-frequency graphs in the test set are subjected to two different random transformations;

[0112] The two different randomly transformed time-frequency graphs are input into the teacher network and student network of the self-supervised training ViT network respectively to obtain two output features;

[0113] The two output features are normalized using the softmax function to obtain the self-supervised interference features.

[0114] In this embodiment, during the training process of the self-supervised training ViT network, the loss function is loss = -P2logP1, where P1 is the output of the teacher network, P2 is the output of the student network, and the stop gradient operator is applied to the teacher network, and the gradient is only propagated through the student network. The teacher parameters are updated using the exponential moving average (EMA) of the student network parameters; the optimizer uses AdamW, and the learning rate uses cosine scheduling to decay the learning rate to minimize the loss value of the self-supervised training ViT network.

[0115] In this embodiment, the teacher network parameters are fixed, and the teacher network is iterated by the gradient update of the student network, which simplifies the gradient update and makes network training easier. Moreover, self-supervised training is used in the training process of the ViT network, and no labeled data is required, which saves a lot of manpower and time.

[0116] In order to detect and locate interference, in one embodiment, interference location processing is performed on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference location frame, specifically including:

[0117] According to the pixel value of each pixel point in the self-supervised interference feature map and the preset pixel threshold, the pixel points above the pixel threshold are extracted as interference positioning frames, thereby obtaining a self-supervised interference feature map with interference positioning frames.

[0118] In this embodiment, interference suppression is performed on the time-frequency graph based on the self-supervised interference feature graph with the interference localization frame, specifically including:

[0119] According to the self-supervised interference feature map with the interference positioning frame, the coordinates of the interference positioning frame are obtained;

[0120] According to the coordinates of the interference positioning frame, the self-supervised interference feature map with the interference positioning frame is segmented to obtain the interference segmentation result map;

[0121] According to the interference segmentation result map, an interference mask map corresponding to the interference in the time-frequency map is obtained;

[0122] According to the interference mask, the interference is filtered out from the time-frequency diagram.

[0123] This embodiment uses an image segmentation algorithm to improve the efficiency of interference suppression.

[0124] Specifically, based on the self-supervised interference feature map with the interference positioning box, an image segmentation algorithm is applied to extract the foreground target, add mask processing in the subsequent processing, add a mask to the generated image and use the mask to suppress interference.

[0125] It is important to know that in the image segmentation algorithm, a Gaussian mixture model (GMM) is used to model the background and foreground separately, and undefined pixels are marked as possible foreground or possible background; each pixel in the image is connected to the surrounding pixels through virtual edges, and based on the similarity between the pixel and the connected pixels, all of its virtual edges are assigned probabilities of belonging to the foreground or background respectively; by statistically analyzing the probabilities of the virtual edges of the pixels, it is determined whether the pixel belongs to the foreground. Each pixel will be classified as the foreground or background. After all the pixels are connected (possibly connected to the background or foreground), if there is a virtual edge that belongs to two regions at the same time (that is, two pixels connected by a virtual edge, one of which belongs to the foreground and the other belongs to the background), the virtual edge between them will be cut off. After performing similar operations on all pixels, the foreground and background of the image can be segmented.

[0126] The present invention also proposes a radar active interference suppression system based on self-supervised training ViT network, comprising:

[0127] An acquisition module, used to obtain a radar active interference time-frequency atlas without interference type labels;

[0128] The training set construction module is used to extract radar active interference time-frequency maps from the radar active interference time-frequency map set without interference type labels and combine it with the ImageNet dataset to form a training set;

[0129] A test set construction module is used to form a test set from the remaining radar active interference time-frequency graphs in the radar active interference time-frequency graph set without interference type labels;

[0130] Model building module, used to build self-supervised training ViT network;

[0131] A training module is used to train the self-supervised training ViT network using the training set to obtain a trained self-supervised training ViT network;

[0132] The testing module is used to extract interference features from the time-frequency graph in the test set using the trained self-supervised training ViT network to obtain a self-supervised interference feature graph;

[0133] A positioning module is used to perform interference positioning processing on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference positioning frame;

[0134] The suppression module is used to suppress interference on the time-frequency map based on the self-supervised interference feature map with the interference localization box.

[0135] It should be noted that the function of each module in this embodiment is the same as the corresponding description in the above method embodiment, and will not be repeated here.

[0136] The present invention also proposes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the radar active interference suppression method based on the self-supervised training ViT network as described in any one of the above is implemented.

[0137] The present invention also proposes an electronic device, comprising: a processor and a memory, the memory being used to store one or more programs; when the one or more programs are executed by the processor, the radar active interference suppression method based on the self-supervised training ViT network as described in any one of the above is implemented.

[0138] The effects of the present invention can be further illustrated by the following examples and comparative examples.

[0139] Example 1

[0140] The interference types of the time-frequency diagrams in the test set in this embodiment include five single interference types: ISRJOF, ISFSRJ, MDFJ, NUISRJ and MISRJ, and four composite interference types: ISFSRJ and NUISRJ, MDFJ and NUISRJ, ISFSRJ and MISRJ, and ISRJOF and NUISRJ.

[0141] This embodiment uses the radar active interference suppression method based on self-supervised training ViT network proposed by the present invention to extract features and locate the selected interference. The feature extraction results are as follows: Figure 4 As shown in (d), the positioning results are as follows Figure 5 (d) shown.

[0142] Comparative Example 1

[0143] The test set in this embodiment is the same as the test set in Example 1.

[0144] This embodiment uses the LOST network method to extract and locate interference features. The feature extraction results are as follows: Figure 4 As shown in (a), the positioning results are as follows Figure 5 As shown in (a).

[0145] Comparative Example 2

[0146] The test set in this embodiment is the same as the test set in Example 1.

[0147] This embodiment uses the DSS network method to extract and locate interference features. The feature extraction results are as follows: Figure 4 As shown in (b), the positioning results are as follows Figure 5 (b) shown.

[0148] Comparative Example 3

[0149] The test set in this embodiment is the same as the test set in Example 1.

[0150] This embodiment uses the TokenCut network method to extract and locate interference features. The feature extraction results are as follows: Figure 4 As shown in (c), the positioning results are as follows Figure 5 (c) shown.

[0151] Example 2

[0152] The time-frequency diagram with interference selected in this embodiment is as follows Figure 6 As shown in (a), the pulse compression result of the time-frequency diagram with interference is as follows Figure 7 As shown in (a).

[0153] This embodiment adopts the radar active interference suppression method based on self-supervised training ViT network proposed by the present invention to filter out interference from the selected time-frequency graph with interference. The time-frequency graph after interference filtering is as follows: Figure 6 As shown in (f), the pulse compression result after interference filtering is as follows Figure 7 (f) shown.

[0154] Comparative Example 4

[0155] The time-frequency diagram with interference in this embodiment is the same as the time-frequency diagram with interference in Example 2.

[0156] This embodiment uses the subspace projection (SSL) method to reconstruct and cancel the time-frequency graph with interference to filter out the interference. The time-frequency graph after interference filtering is as follows: Figure 6 As shown in (b), the pulse compression result after interference filtering is as follows Figure 7 (b) shown.

[0157] Comparative Example 5

[0158] The time-frequency diagram with interference in this embodiment is the same as the time-frequency diagram with interference in Example 2.

[0159] This embodiment uses a Bayesian Compressed Sensing (BCS) method to filter out the time-frequency graph with interference. The time-frequency graph after interference filtering is as follows: Figure 6 As shown in (c), the pulse compression result after interference filtering is as follows Figure 7 (c) shown.

[0160] Comparative Example 6

[0161] The time-frequency diagram with interference in this embodiment is the same as the time-frequency diagram with interference in Example 2.

[0162] The adaptive filter method of this embodiment filters the time-frequency graph with interference to suppress the interference. The time-frequency graph after the interference is filtered out is as follows: Figure 6 As shown in (d), the pulse compression result after interference filtering is as follows Figure 7 (d) shown.

[0163] Comparative Example 7

[0164] The time-frequency diagram with interference in this embodiment is the same as the time-frequency diagram with interference in Example 2.

[0165] This embodiment uses the TokenCut network method to extract and locate the time-frequency graph with interference, and uses the extracted interference features to form a mask to suppress interference. The filtering effect is as follows: Figure 6 As shown in (e), the pulse compression result after interference filtering is as follows Figure 7 (e) shown.

[0166] from Figure 4 and Figure 5 It can be seen from the figure that the radar active interference suppression method based on the self-supervised training ViT network proposed in the present invention shows good results in both interference feature extraction and interference positioning.

[0167] Depend on Figure 6 and Figure 7 It can be seen from the figure that the radar active interference suppression method based on the self-supervised training ViT network proposed in the present invention not only suppresses interference well, but also has the best protection for the target signal.

[0168] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A radar active jamming suppression method based on self-supervised training ViT network, characterized in that: include: Obtain radar active interference time-frequency atlas without interference type labels; Extract radar active interference time-frequency maps from the radar active interference time-frequency map collection without interference type labels, and combine them with the natural dataset ImageNet to form a training set; The remaining radar active interference time-frequency graphs in the radar active interference time-frequency graph set without interference type labels are combined into a test set; Construct a self-supervised training ViT network; the self-supervised training ViT network includes a teacher network and a student network. Both the teacher network and the student network use the VIT-tiny network model as the base model, and use a focused linear attention module to replace the self-attention module in the softmax function of the VIT-tiny network. Use the training set to train the self-supervised training ViT network to obtain a trained self-supervised training ViT network; The trained self-supervised ViT network is used to extract interference features from the time-frequency graph in the test set to obtain a self-supervised interference feature graph; Perform interference location processing on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference location frame; Interference suppression is performed on the time-frequency map based on the self-supervised interference feature map with interference localization boxes.

2. The radar active interference suppression method based on self-supervised training ViT network according to claim 1 is characterized in that The VIT-tiny network model consists of an image segmentation module, a patch encoding module, and a transformer encoder connected in sequence. Among them, the transformer encoder includes 6 transformer layers connected in sequence; among them, each transformer layer includes a first normalization layer, a multi-head self-attention layer, a second normalization layer and a multi-layer perceptron, the input end of the first normalization layer serves as the input end of the transformer layer, the output end of the first normalization layer is connected to the input end of the multi-head self-attention layer, and the input end of the first normalization layer and the output end of the multi-head self-attention layer are added and connected to the input end of the second normalization layer, the output end of the second normalization layer is connected to the input end of the multi-layer perceptron, and the input end of the second normalization layer and the output end of the multi-layer perceptron are added and connected as the output end of the transformer layer.

3. The radar active interference suppression method based on self-supervised training ViT network according to claim 1 is characterized in that The trained self-supervised ViT network is used to extract interference features from the time-frequency graph in the test set to obtain a self-supervised interference feature map, which specifically includes: The time-frequency graphs in the test set are subjected to two different random transformations; The two different randomly transformed time-frequency graphs are input into the teacher network and student network of the self-supervised training ViT network respectively to obtain two output features; The two output features are normalized using the softmax function to obtain the self-supervised interference features.

4. The radar active interference suppression method based on self-supervised training ViT network according to claim 1 is characterized in that During the self-supervised training of the ViT network, the loss function is loss = -P2logP1, where P1 is the output of the teacher network and P2 is the output of the student network. The stop gradient operator is applied to the teacher network, and the gradient is only propagated through the student network. The teacher parameters are updated using the exponential moving average of the student network parameters; the optimizer uses AdamW, and the learning rate uses cosine scheduling to decay the learning rate.

5. The radar active interference suppression method based on self-supervised training ViT network according to claim 1 is characterized in that: Based on the self-supervised interference feature map with the interference localization frame, interference suppression is performed on the time-frequency map, specifically including: According to the self-supervised interference feature map with the interference positioning frame, the coordinates of the interference positioning frame are obtained; According to the coordinates of the interference positioning frame, the self-supervised interference feature map with the interference positioning frame is segmented to obtain the interference segmentation result map; According to the interference segmentation result map, an interference mask map corresponding to the interference in the time-frequency map is obtained; According to the interference mask, the interference is filtered out from the time-frequency diagram.

6. A radar active jamming suppression system based on self-supervised training ViT network, characterized in that: include: An acquisition module is used to obtain radar active interference time-frequency atlases without interference type labels and supervised time-frequency atlases with interference type labels; The training set construction module is used to extract radar active interference time-frequency maps from the radar active interference time-frequency map set without interference type labels and combine it with the ImageNet dataset to form a training set; A test set construction module is used to form a test set from the remaining radar active interference time-frequency graphs in the radar active interference time-frequency graph set without interference type labels; Model building module, used to build self-supervised training ViT network; A training module is used to train the self-supervised training ViT network using the training set to obtain a trained self-supervised training ViT network; The testing module is used to extract interference features from the time-frequency graph in the test set using the trained self-supervised training ViT network to obtain a self-supervised interference feature graph; A positioning module is used to perform interference positioning processing on the self-supervised interference feature map to obtain a self-supervised interference feature map with an interference positioning frame; The suppression module is used to suppress interference on the time-frequency map based on the self-supervised interference feature map with the interference localization box.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the radar active interference suppression method based on the self-supervised training ViT network as described in any one of claims 1 to 5 is implemented.

8. An electronic device comprising: A processor and a memory, the memory being used to store one or more programs, characterized in that when the one or more programs are executed by the processor, the radar active interference suppression method based on the self-supervised training ViT network as described in any one of claims 1 to 5 is implemented.

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