Pulse‐level work state recognition of multifunction radar based on MC‐RSG
Abstract Accurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse‐level work state recognition method of MFR based on the residual block with spatial attention connected ga...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12609 |
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| _version_ | 1846148693939453952 |
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| author | Zijun Qin Wenjuan Ren Zhanpeng Yang Xian Sun |
| author_facet | Zijun Qin Wenjuan Ren Zhanpeng Yang Xian Sun |
| author_sort | Zijun Qin |
| collection | DOAJ |
| description | Abstract Accurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse‐level work state recognition method of MFR based on the residual block with spatial attention connected gated recurrent unit by features using metric coding and correlative embedding (MC‐RSG) is proposed. Metric coding is designed to generate the distance vector with time of arrival, and the correlative embedding is performed on the distance vector and raw data features to increase the feature information by extracting feature information associated with the previous and subsequent pulses in each feature sequence, respectively. Besides, we make use of the model called RSG containing the residual block with spatial attention connected gated recurrent unit to learn the features of pulse sequences and identify the radar work state label of each pulse. The experimental work shows that the method is robust and has achieved up to 97% recognition accuracy on the test dataset under ideal observation conditions and 5% higher than the comparison network in high noise observation conditions. |
| format | Article |
| id | doaj-art-b3882f6976084d3dbed835c78475edee |
| institution | Kabale University |
| issn | 1751-8784 1751-8792 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj-art-b3882f6976084d3dbed835c78475edee2024-11-30T14:53:01ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922024-11-0118112108212110.1049/rsn2.12609Pulse‐level work state recognition of multifunction radar based on MC‐RSGZijun Qin0Wenjuan Ren1Zhanpeng Yang2Xian Sun3Aerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAbstract Accurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse‐level work state recognition method of MFR based on the residual block with spatial attention connected gated recurrent unit by features using metric coding and correlative embedding (MC‐RSG) is proposed. Metric coding is designed to generate the distance vector with time of arrival, and the correlative embedding is performed on the distance vector and raw data features to increase the feature information by extracting feature information associated with the previous and subsequent pulses in each feature sequence, respectively. Besides, we make use of the model called RSG containing the residual block with spatial attention connected gated recurrent unit to learn the features of pulse sequences and identify the radar work state label of each pulse. The experimental work shows that the method is robust and has achieved up to 97% recognition accuracy on the test dataset under ideal observation conditions and 5% higher than the comparison network in high noise observation conditions.https://doi.org/10.1049/rsn2.12609multifunction radarradar emitter recognitionradar signal processingradar target recognition |
| spellingShingle | Zijun Qin Wenjuan Ren Zhanpeng Yang Xian Sun Pulse‐level work state recognition of multifunction radar based on MC‐RSG IET Radar, Sonar & Navigation multifunction radar radar emitter recognition radar signal processing radar target recognition |
| title | Pulse‐level work state recognition of multifunction radar based on MC‐RSG |
| title_full | Pulse‐level work state recognition of multifunction radar based on MC‐RSG |
| title_fullStr | Pulse‐level work state recognition of multifunction radar based on MC‐RSG |
| title_full_unstemmed | Pulse‐level work state recognition of multifunction radar based on MC‐RSG |
| title_short | Pulse‐level work state recognition of multifunction radar based on MC‐RSG |
| title_sort | pulse level work state recognition of multifunction radar based on mc rsg |
| topic | multifunction radar radar emitter recognition radar signal processing radar target recognition |
| url | https://doi.org/10.1049/rsn2.12609 |
| work_keys_str_mv | AT zijunqin pulselevelworkstaterecognitionofmultifunctionradarbasedonmcrsg AT wenjuanren pulselevelworkstaterecognitionofmultifunctionradarbasedonmcrsg AT zhanpengyang pulselevelworkstaterecognitionofmultifunctionradarbasedonmcrsg AT xiansun pulselevelworkstaterecognitionofmultifunctionradarbasedonmcrsg |