Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar
Precise identification of active jamming in complex electromagnetic environments remains critically challenging for cognitive radar systems. Current methods often exhibit limitations in insufficient feature extraction and underutilization of jamming signals, leading to substantial performance degrad...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1723 |
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| author | Xiaoying Chen Ying Liu Cheng Wang |
| author_facet | Xiaoying Chen Ying Liu Cheng Wang |
| author_sort | Xiaoying Chen |
| collection | DOAJ |
| description | Precise identification of active jamming in complex electromagnetic environments remains critically challenging for cognitive radar systems. Current methods often exhibit limitations in insufficient feature extraction and underutilization of jamming signals, leading to substantial performance degradation, particularly in low jamming-to-noise ratio (JNR) scenarios. To address these challenges, we propose a novel framework based on a multi-domain fusion network, MDFNet, to recognize 12 types of active jamming signals. MDFNet improves the recognition robustness under varying JNR conditions through a two-stage fusion of complementary features from pulse compression time–frequency (PC-TF) and range-Doppler (RD) domain images. Specifically, a novel dual-modal feature fusion (DMFF) module integrates PC-TF and RD features, while a decision fusion strategy leverages their distinctive characteristics. Experiments on typical jamming dataset demonstrate that MDFNet achieves an overall recognition accuracy of 96.05%. Notably, at a JNR of −20 dB, MDFNet outperforms the existing fusion methods by 12.86–18.19%. In summary, our proposed method significantly enhances the jamming recognition capability of cognitive radar systems in complex environments. |
| format | Article |
| id | doaj-art-29068fde7e984c74897baecb835260e8 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-29068fde7e984c74897baecb835260e82025-08-20T02:34:02ZengMDPI AGRemote Sensing2072-42922025-05-011710172310.3390/rs17101723Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive RadarXiaoying Chen0Ying Liu1Cheng Wang2University of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaBeijing Raying Technology, Co., Ltd., Beijing 100080, ChinaPrecise identification of active jamming in complex electromagnetic environments remains critically challenging for cognitive radar systems. Current methods often exhibit limitations in insufficient feature extraction and underutilization of jamming signals, leading to substantial performance degradation, particularly in low jamming-to-noise ratio (JNR) scenarios. To address these challenges, we propose a novel framework based on a multi-domain fusion network, MDFNet, to recognize 12 types of active jamming signals. MDFNet improves the recognition robustness under varying JNR conditions through a two-stage fusion of complementary features from pulse compression time–frequency (PC-TF) and range-Doppler (RD) domain images. Specifically, a novel dual-modal feature fusion (DMFF) module integrates PC-TF and RD features, while a decision fusion strategy leverages their distinctive characteristics. Experiments on typical jamming dataset demonstrate that MDFNet achieves an overall recognition accuracy of 96.05%. Notably, at a JNR of −20 dB, MDFNet outperforms the existing fusion methods by 12.86–18.19%. In summary, our proposed method significantly enhances the jamming recognition capability of cognitive radar systems in complex environments.https://www.mdpi.com/2072-4292/17/10/1723jamming recognitioncognitive radarmulti-domain feature fusiondecision fusion |
| spellingShingle | Xiaoying Chen Ying Liu Cheng Wang Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar Remote Sensing jamming recognition cognitive radar multi-domain feature fusion decision fusion |
| title | Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar |
| title_full | Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar |
| title_fullStr | Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar |
| title_full_unstemmed | Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar |
| title_short | Multi-Domain Fusion Network for Active Jamming Recognition in Cognitive Radar |
| title_sort | multi domain fusion network for active jamming recognition in cognitive radar |
| topic | jamming recognition cognitive radar multi-domain feature fusion decision fusion |
| url | https://www.mdpi.com/2072-4292/17/10/1723 |
| work_keys_str_mv | AT xiaoyingchen multidomainfusionnetworkforactivejammingrecognitionincognitiveradar AT yingliu multidomainfusionnetworkforactivejammingrecognitionincognitiveradar AT chengwang multidomainfusionnetworkforactivejammingrecognitionincognitiveradar |