Radar active deception jamming recognition based on Siamese squeeze wavelet attention network
Abstract Active deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12482 |
| _version_ | 1797394708984496128 |
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| author | Zhenhua Wu Tengxin Wang Yice Cao Man Zhang Lixia Yang |
| author_facet | Zhenhua Wu Tengxin Wang Yice Cao Man Zhang Lixia Yang |
| author_sort | Zhenhua Wu |
| collection | DOAJ |
| description | Abstract Active deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios. |
| first_indexed | 2024-03-09T00:23:40Z |
| format | Article |
| id | doaj.art-cc685cbeaf734937aab4a2d114ef4f05 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-03-09T00:23:40Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-cc685cbeaf734937aab4a2d114ef4f052023-12-12T05:20:23ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922023-12-0117121886189810.1049/rsn2.12482Radar active deception jamming recognition based on Siamese squeeze wavelet attention networkZhenhua Wu0Tengxin Wang1Yice Cao2Man Zhang3Lixia Yang4Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei ChinaSchool of Electronics and Communication Engineering Guangzhou University Guangzhou ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei ChinaAbstract Active deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios.https://doi.org/10.1049/rsn2.12482radar interferenceradar signal processingwavelet transforms |
| spellingShingle | Zhenhua Wu Tengxin Wang Yice Cao Man Zhang Lixia Yang Radar active deception jamming recognition based on Siamese squeeze wavelet attention network IET Radar, Sonar & Navigation radar interference radar signal processing wavelet transforms |
| title | Radar active deception jamming recognition based on Siamese squeeze wavelet attention network |
| title_full | Radar active deception jamming recognition based on Siamese squeeze wavelet attention network |
| title_fullStr | Radar active deception jamming recognition based on Siamese squeeze wavelet attention network |
| title_full_unstemmed | Radar active deception jamming recognition based on Siamese squeeze wavelet attention network |
| title_short | Radar active deception jamming recognition based on Siamese squeeze wavelet attention network |
| title_sort | radar active deception jamming recognition based on siamese squeeze wavelet attention network |
| topic | radar interference radar signal processing wavelet transforms |
| url | https://doi.org/10.1049/rsn2.12482 |
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