Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model
As a typical and widely used passive jamming method, chaff clouds have a strong interference effect on radar that remains a significant challenge effectively to counteract. It is exceedingly necessary to improve the anti-chaff jamming ability of radars. In this paper, we address this challenge by pr...
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MDPI AG
2025-04-01
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| author | Shuolei Li Bin Liu Lin Zhou Jingping Liu |
| author_facet | Shuolei Li Bin Liu Lin Zhou Jingping Liu |
| author_sort | Shuolei Li |
| collection | DOAJ |
| description | As a typical and widely used passive jamming method, chaff clouds have a strong interference effect on radar that remains a significant challenge effectively to counteract. It is exceedingly necessary to improve the anti-chaff jamming ability of radars. In this paper, we address this challenge by proposing an effective residual attention network named RA-Net. Specifically, we introduce an attention mechanism that enables the network to focus on the most informative and stable hierarchical features of the high-resolution range profile (HRRP) data, significantly improving the model’s feature extraction capability and overall performance. In addition, we address the limitation of insufficient measured chaff cloud echo data by establishing a remarkably rich and diverse data set of chaff cloud HRRP data through extensive field experiments. This dataset serves as a valuable resource and a critical foundation for advancing HRRP recognition research in this domain. Experimental results on measured HRRP data demonstrate that RA-Net achieves superior recognition accuracy of 97.10%, outperforming traditional methods, and also exhibits outstanding generalization capability. These results establish RA-Net as a new benchmark for chaff cloud HRRP recognition. |
| format | Article |
| id | doaj-art-db30f57835f54b79a3c7331920cc1dc6 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-db30f57835f54b79a3c7331920cc1dc62025-08-20T02:31:08ZengMDPI AGSensors1424-82202025-04-01259266310.3390/s25092663Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention ModelShuolei Li0Bin Liu1Lin Zhou2Jingping Liu3School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaAs a typical and widely used passive jamming method, chaff clouds have a strong interference effect on radar that remains a significant challenge effectively to counteract. It is exceedingly necessary to improve the anti-chaff jamming ability of radars. In this paper, we address this challenge by proposing an effective residual attention network named RA-Net. Specifically, we introduce an attention mechanism that enables the network to focus on the most informative and stable hierarchical features of the high-resolution range profile (HRRP) data, significantly improving the model’s feature extraction capability and overall performance. In addition, we address the limitation of insufficient measured chaff cloud echo data by establishing a remarkably rich and diverse data set of chaff cloud HRRP data through extensive field experiments. This dataset serves as a valuable resource and a critical foundation for advancing HRRP recognition research in this domain. Experimental results on measured HRRP data demonstrate that RA-Net achieves superior recognition accuracy of 97.10%, outperforming traditional methods, and also exhibits outstanding generalization capability. These results establish RA-Net as a new benchmark for chaff cloud HRRP recognition.https://www.mdpi.com/1424-8220/25/9/2663chaff jammingdatasetradarHRRPresidualattention mechanism |
| spellingShingle | Shuolei Li Bin Liu Lin Zhou Jingping Liu Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model Sensors chaff jamming dataset radar HRRP residual attention mechanism |
| title | Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model |
| title_full | Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model |
| title_fullStr | Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model |
| title_full_unstemmed | Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model |
| title_short | Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model |
| title_sort | anti chaff jamming method of radar based on real dataset and residual attention model |
| topic | chaff jamming dataset radar HRRP residual attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/9/2663 |
| work_keys_str_mv | AT shuoleili antichaffjammingmethodofradarbasedonrealdatasetandresidualattentionmodel AT binliu antichaffjammingmethodofradarbasedonrealdatasetandresidualattentionmodel AT linzhou antichaffjammingmethodofradarbasedonrealdatasetandresidualattentionmodel AT jingpingliu antichaffjammingmethodofradarbasedonrealdatasetandresidualattentionmodel |