Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition
With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming sig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/12/374 |
| _version_ | 1797380954085392384 |
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| author | Minghui Sha Dewu Wang Fei Meng Wenyan Wang Yu Han |
| author_facet | Minghui Sha Dewu Wang Fei Meng Wenyan Wang Yu Han |
| author_sort | Minghui Sha |
| collection | DOAJ |
| description | With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from −16 dB to −8 dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from −16 dB to −8 dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments. |
| first_indexed | 2024-03-08T20:45:22Z |
| format | Article |
| id | doaj.art-0cbb141fd3ae47bca5e75b35121c9aa1 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-08T20:45:22Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-0cbb141fd3ae47bca5e75b35121c9aa12023-12-22T14:10:08ZengMDPI AGFuture Internet1999-59032023-11-01151237410.3390/fi15120374Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming RecognitionMinghui Sha0Dewu Wang1Fei Meng2Wenyan Wang3Yu Han4Beijing Institute of Radio Measurement, Beijing 100854, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Institute of Radio Measurement, Beijing 100854, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaWith the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from −16 dB to −8 dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from −16 dB to −8 dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments.https://www.mdpi.com/1999-5903/15/12/374radar jamming recognitionvision transformerdiffusion modeltime-frequency analysis |
| spellingShingle | Minghui Sha Dewu Wang Fei Meng Wenyan Wang Yu Han Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition Future Internet radar jamming recognition vision transformer diffusion model time-frequency analysis |
| title | Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition |
| title_full | Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition |
| title_fullStr | Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition |
| title_full_unstemmed | Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition |
| title_short | Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition |
| title_sort | diff swint an integrated framework of diffusion model and swin transformer for radar jamming recognition |
| topic | radar jamming recognition vision transformer diffusion model time-frequency analysis |
| url | https://www.mdpi.com/1999-5903/15/12/374 |
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