Radar Jamming Recognition: Models, Methods, and Prospects
In modern warfare with complex and changeable electromagnetic environments, radar jamming is getting more complex and realistic, which poses a serious threat to radar; jamming recognition has become a hot topic in the field of electronic countermeasures. To make effective antijamming measures, numer...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10816388/ |
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| author | Zan Wang Zhengwei Guo Gaofeng Shu Ning Li |
| author_facet | Zan Wang Zhengwei Guo Gaofeng Shu Ning Li |
| author_sort | Zan Wang |
| collection | DOAJ |
| description | In modern warfare with complex and changeable electromagnetic environments, radar jamming is getting more complex and realistic, which poses a serious threat to radar; jamming recognition has become a hot topic in the field of electronic countermeasures. To make effective antijamming measures, numerous jamming recognition methods have been proposed. This article presents a systematic review of jamming recognition for this topic. Specifically, first building a system framework for jamming models, including deception jamming, suppression jamming, and smart jamming, thoroughly explaining the operational mechanisms. Then, recognition methods based on traditional machine learning are summarized and are delved into the advantages and disadvantages of feature extraction methods and classifiers. Furthermore, the focus shifts to neural network-based methods, such as shallow neural network methods and deep neural network methods. In particular, restricted sample strategies are also discussed as potential future directions. Finally, conclusions on the current status of jamming recognition methods and the prospects for future work are made. This article provides a reference for the research of radar jamming recognition. |
| format | Article |
| id | doaj-art-65ba4cd069814219842a138dfa4fdb15 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-65ba4cd069814219842a138dfa4fdb152025-01-21T00:00:48ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183315334310.1109/JSTARS.2024.352295110816388Radar Jamming Recognition: Models, Methods, and ProspectsZan Wang0Zhengwei Guo1https://orcid.org/0009-0007-6283-5504Gaofeng Shu2https://orcid.org/0000-0002-7098-7029Ning Li3https://orcid.org/0000-0002-4358-6449School of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaIn modern warfare with complex and changeable electromagnetic environments, radar jamming is getting more complex and realistic, which poses a serious threat to radar; jamming recognition has become a hot topic in the field of electronic countermeasures. To make effective antijamming measures, numerous jamming recognition methods have been proposed. This article presents a systematic review of jamming recognition for this topic. Specifically, first building a system framework for jamming models, including deception jamming, suppression jamming, and smart jamming, thoroughly explaining the operational mechanisms. Then, recognition methods based on traditional machine learning are summarized and are delved into the advantages and disadvantages of feature extraction methods and classifiers. Furthermore, the focus shifts to neural network-based methods, such as shallow neural network methods and deep neural network methods. In particular, restricted sample strategies are also discussed as potential future directions. Finally, conclusions on the current status of jamming recognition methods and the prospects for future work are made. This article provides a reference for the research of radar jamming recognition.https://ieeexplore.ieee.org/document/10816388/Feature extractionlimited sample strategiesneural networkradar jamming recognitiontraditional machine learning |
| spellingShingle | Zan Wang Zhengwei Guo Gaofeng Shu Ning Li Radar Jamming Recognition: Models, Methods, and Prospects IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Feature extraction limited sample strategies neural network radar jamming recognition traditional machine learning |
| title | Radar Jamming Recognition: Models, Methods, and Prospects |
| title_full | Radar Jamming Recognition: Models, Methods, and Prospects |
| title_fullStr | Radar Jamming Recognition: Models, Methods, and Prospects |
| title_full_unstemmed | Radar Jamming Recognition: Models, Methods, and Prospects |
| title_short | Radar Jamming Recognition: Models, Methods, and Prospects |
| title_sort | radar jamming recognition models methods and prospects |
| topic | Feature extraction limited sample strategies neural network radar jamming recognition traditional machine learning |
| url | https://ieeexplore.ieee.org/document/10816388/ |
| work_keys_str_mv | AT zanwang radarjammingrecognitionmodelsmethodsandprospects AT zhengweiguo radarjammingrecognitionmodelsmethodsandprospects AT gaofengshu radarjammingrecognitionmodelsmethodsandprospects AT ningli radarjammingrecognitionmodelsmethodsandprospects |