空天防御2026，Vol.9Issue(1)：36-45,10.
基于深度学习时序特征增强的海杂波抑制方法
Sea Clutter Suppression Method Based on Deep Learning Temporal Feature Enhancement
摘要
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.关键词
海杂波抑制/时序特征增强/时序压缩/双向ConvLSTM/时间注意力机制Key words
sea clutter suppression/temporal feature enhancement/temporal compression/bidirectional ConvLSTM/temporal attention mechanism分类
信息技术与安全科学引用本文复制引用
刘轩,兰晓宸,徐大鹏,何良,刘茂珅..基于深度学习时序特征增强的海杂波抑制方法[J].空天防御,2026,9(1):36-45,10.基金项目
自动目标识别全国重点实验室基础研究资助项目 （）
