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| Sea Clutter Suppression Method Based on Deep Learning Temporal Feature Enhancement |
| LIU Xuan1， LAN Xiaochen2， XU Dapeng1， HE Liang1， LIU Maoshen1 |
| 1. Beijing Huahang Radio Measurement & Research Institute， Beijing 100013， China；
2. 93160 of the People’s Liberation Army， Beijing 100074， China |
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Abstract To overcome the limitations of traditional approaches in suppressing sea clutter which displays complex, non-stationary, non-Gaussian distributions and strong temporal correlation, this paper introduces RTFEN (Radar Temporal Feature Enhancement Network), a deep learning method that improves temporal feature extraction. The technique employs a temporal compression module to suppress clutter via space-time filtering, a bidirectional ConvLSTM to model motion differences between targets and clutter, and a temporal attention mechanism to adaptively enhance target information in key frames. Experimental results show that the proposed method can effectively enhance sea-clutter suppression performance.
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Received: 17 December 2025
Published: 11 March 2026
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2026, Vol. 9 
