Jamming Recognition Based on AC-VAEGAN

Tang, Yan; Zhao, Zhijin; Ye, Xueyi; Zheng, Shilian; Wang, Lijun

doi:10.1109/icsp48669.2020.9320987

Cited by 16 publications

(12 citation statements)

References 4 publications

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“…Each jamming signal was generated with 100 samples at each jamming-to-noise ratio (JNR) as training data, and 400 samples were generated as test data. In this paper, the JNR is defined as [37] G JN R = 10 lg(P J /P N ),…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Distributed Few-Shot Learning for Intelligent Recognition of Communication Jamming

Liu

et al. 2022

IEEE J. Sel. Top. Signal Process.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Distributed Few-Shot Learning for Intelligent Recognition of Communication Jamming

Liu

et al. 2022

IEEE J. Sel. Top. Signal Process.

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“…Jamming recognition network [9] based on power spectrum features was proposed to recognize ten kinds of suppression jamming signals. Siamese-CNN [10] and auxiliary classifier variational auto-encoding generative adversarial network [11] were proposed to solve the performance deterioration of the interference signals recognition method in the case of a small sample set. The Auto-Encoder network, which was built by stacking long short-term memory, separated the interference signal from the transmitted signal, and another recurrent neural network realized interference signals recognition [12].…”

Section: Interference Signal Recognition Based On Deep Learningmentioning

confidence: 99%

“…Deep learning has achieved excellent performance in natural language processing [1,2] and computer vision [3][4][5]. Therefore, many recognition algorithms based on deep learning for interference signals were proposed to solve the problems of traditional interference signal recognition algorithms whose accuracy is low and significantly affected by artificial feature selection [6][7][8][9][10][11][12]. However, most interference signals recognition algorithms belong to supervised learning that requires labeled signals samples.…”

Section: Introductionmentioning

confidence: 99%

Contrastive Clustering for Unsupervised Recognition of Interference Signals

Chen¹,

Zhao²,

Ye³

et al. 2023

Computer Systems Science and Engineering

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Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning.In the second phase, the original dataset's k-nearest neighbor set (KNNset) is designed in double dimensions contrastive learning. In addition, a dynamic entropy parameter strategy is proposed. The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies; the feature dimensional contrastive learning in the second phase can improve the clustering purity; the dynamic entropy parameter strategy can improve the stability of DDCC effectively. The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms. In particular, the clustering purity of our method is above 92%, SCAN's is 81%, and the other three methods' are below 71% when jammingnoise-ratio (JNR) is −5 dB. In addition, our method is close to the supervised learning algorithm.

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“…For instance, Ghadimi et al [9] deployed refined GoogleNet and AlexNet models for radar signal detection and classification, while Qin et al [10] utilized ResNet architecture to transform radar signals into time-frequency (TF) images for signal identification, achieving an overall recognition rate of up to 96% for jamming-to-noise ratio (JNR) of −2 dB. Tang et al [11] proposed an jamming signal recognition algorithm based on AC-VAEGAN, which has a higher recognition rate than other algorithms in the case of a tiny number of samples. Meanwhile, Qu et al [12] developed a jamming recognition network (JRNET) based on power spectrum features to resolve the problem of low recognition for jamming signals with low JNR, with experimental outcomes showcasing higher recognition rates than conventional convolutional neural networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

GNSS blanket jamming classification algorithm based on spatial attention mechanism and residual shrinkage neural network

Lin,

Zhuang,

Niu

et al. 2024

Meas. Sci. Technol.

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In the context of an increasingly complex electromagnetic environment, satellite navigation systems have become highly susceptible to jamming. Detecting and classifying jamming has thus become crucial for taking effective anti-jamming measures. This paper addresses the issue that the classification accuracy of blanket jamming declines drastically in low jamming-to-noise ratio (JNR) scenarios. To tackle this challenge, a novel algorithm is proposed that combines the spatial attention mechanism with a residual shrinkage neural network (RSN-SA) to classify 10 types of blanket jamming, ranging from single jamming to convolutional compound jamming. Specifically, the proposed algorithm first employs the Fourier Synchrosqueezed Transform (FSST) to extract time-frequency (TF) domain features from the original jamming signal, generating corresponding TF images. Then, the RSN-SA is employed to identify and classify these images effectively while minimizing the impact of noise-related features. This allows the main parts of the TF images to be focused on, resulting in higher recognition accuracy. Simulation results demonstrate that RSN-SA achieves close to 100% accuracy for six single blanket jamming signals. Moreover, compared with the other five algorithms, RSN-SA effectively enhances the classification accuracy of convolutional compound jamming signals in low JNR scenarios and improves the recognition stability in high JNR scenarios. Overall, the proposed algorithm provides a promising solution for classifying blanket jamming in satellite navigation systems with high accuracy and robustness.

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Jamming Recognition Based on AC-VAEGAN

Cited by 16 publications

References 4 publications

Distributed Few-Shot Learning for Intelligent Recognition of Communication Jamming

Distributed Few-Shot Learning for Intelligent Recognition of Communication Jamming

Contrastive Clustering for Unsupervised Recognition of Interference Signals

GNSS blanket jamming classification algorithm based on spatial attention mechanism and residual shrinkage neural network

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