Recognition of radar active‐jamming through convolutional neural networks

Wang, Yafeng; Sun, Boye; Wang, Ning

doi:10.1049/joe.2019.0659

Cited by 45 publications

(19 citation statements)

References 5 publications

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“…The second part of the CV-CNN is the CV activation function. In this work, three CV activate functions are extended from RV ReLU (i.e., modReLU [16], zReLU [17], and cReLU). The input of the CV-CNN is the CV radar jamming signal, and all mathematical operations of CV-CNN are extended under the theory of complex analysis, including forward propagation and backpropagation.…”

Section: Cv-cnn-based Radar Jamming Signal Recognitionmentioning

confidence: 99%

Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Zhang

Chen

et al. 2021

Remote Sensing

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Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.

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Section: Cv-cnn-based Radar Jamming Signal Recognitionmentioning

confidence: 99%

Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Zhang

Chen

et al. 2021

Remote Sensing

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show abstract

“…At the same time, the number of decision trees in RF was 200 and the number of features to consider when looking for the best split was set to 10. Furthermore, we compared the designed algorithm with the current 2D-CNN method applied to radar jamming classification (we referred to the network structure of [19]). Finally, we used overall accuracy (OA), and kappa coefficient (K) [29] to compare and estimate the capabilities of the proposed models.…”

Section: B Experimental Parameters Settings 1) Comparative Experimentsmentioning

confidence: 99%

“…Such as, Yun et al proposed a new method of barrage jamming detection and classification for SAR based on CNN [18]. Wang et al [19] designed CNN to classify active jamming. However, there are few types of jamming signals that can be distinguish by radar jamming signal classification methods based on CNN, and with the development of electronic technology, more and more jamming patterns are presented.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

2020

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Jamming is a big threat to radar system survival and anti-jamming is a part of the solution. The classification of radar jamming signal is the first step toward to anti-jamming. Recently, as an important part of deep learning, convolutional neural network (CNN) based methods have shown their capability in discriminant feature extraction and accurate classification. In this study, in order to harness the powerfulness of deep learning, CNN based methods are proposed to classify radar jamming signal acting on pulse compression radar. Specifically, a 1D-CNN is designed for radar jamming signal classification under the condition of sufficient training samples. Furthermore, due to the fact that the collection of sufficient training samples is time-consuming and expensive, a CNN-based siamese network is proposed for radar jamming signal classification to deal with the issue of limited training samples. The experimental results with sufficient and limited training samples show that the CNN-based classification methods obtain good classification performance in terms of classification accuracy and show a huge potential for radar jamming signal classification. INDEX TERMS Radar jamming signal, convolutional neural network (CNN), sufficient and limited training samples, siamese network.

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“…In [ 7 ], the researchers used a combined network including CNN and support vector machine (SVM) networks to realize the recognition of the signal modulation type of low probability of intercept (LPI) radar and achieved good results. In [ 8 , 9 , 10 , 11 ], the researchers have all tried to use CNN network for radar jamming recognition. However, the above research was merely a simple application of simple neural networks.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion

Lan

Wan

Jiang

et al. 2021

Sensors

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The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo and achieves the effect of hiding the real target. Traditional deceptive jamming recognition methods need to extract complex features and artificially set classification thresholds, which is inefficient. The existing neural network-based jamming identification methods still follow the pattern of signal modulation-type identification, so there are fewer types of jamming that can be identified, and the identification accuracy is low in the case of low jamming-to-noise ratios (JNR). This paper studies the input of jamming recognition networks and proposes an improved intelligent identification method for chirp radar deceptive jamming. This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). Using a CNN to classify the time–frequency image has realized the recognition of a variety of common deceptive jamming techniques. Similarly, by changing the network input, the original signal is used to replace the echo signal, which improves the accuracy of the jamming recognition in the case of a low JNR.

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Recognition of radar active‐jamming through convolutional neural networks

Cited by 45 publications

References 5 publications

Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion

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