Online unsupervised generative learning framework based radar jamming waveform design
Abstract The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the ra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-10-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12433 |
| _version_ | 1827797379275816960 |
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| author | Yuzheng Sun Shuaige Gong Yu Mao Yang‐Yang Dong Chun‐Xi Dong |
| author_facet | Yuzheng Sun Shuaige Gong Yu Mao Yang‐Yang Dong Chun‐Xi Dong |
| author_sort | Yuzheng Sun |
| collection | DOAJ |
| description | Abstract The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one. |
| first_indexed | 2024-03-11T19:21:20Z |
| format | Article |
| id | doaj.art-a061a26cfed24ac0923c6119f073c007 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-03-11T19:21:20Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-a061a26cfed24ac0923c6119f073c0072023-10-07T08:00:40ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922023-10-0117101441145510.1049/rsn2.12433Online unsupervised generative learning framework based radar jamming waveform designYuzheng Sun0Shuaige Gong1Yu Mao2Yang‐Yang Dong3Chun‐Xi Dong4State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Luoyang ChinaState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Luoyang ChinaThe 723 Institute of CSSC Yangzhou Jiangsu ChinaSchool of Electronic Engineering Xidian University Xi'an Shaanxi ChinaSchool of Electronic Engineering Xidian University Xi'an Shaanxi ChinaAbstract The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one.https://doi.org/10.1049/rsn2.12433adaptive signal processingradar jammingwaveform generators |
| spellingShingle | Yuzheng Sun Shuaige Gong Yu Mao Yang‐Yang Dong Chun‐Xi Dong Online unsupervised generative learning framework based radar jamming waveform design IET Radar, Sonar & Navigation adaptive signal processing radar jamming waveform generators |
| title | Online unsupervised generative learning framework based radar jamming waveform design |
| title_full | Online unsupervised generative learning framework based radar jamming waveform design |
| title_fullStr | Online unsupervised generative learning framework based radar jamming waveform design |
| title_full_unstemmed | Online unsupervised generative learning framework based radar jamming waveform design |
| title_short | Online unsupervised generative learning framework based radar jamming waveform design |
| title_sort | online unsupervised generative learning framework based radar jamming waveform design |
| topic | adaptive signal processing radar jamming waveform generators |
| url | https://doi.org/10.1049/rsn2.12433 |
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