雷达科学与技术2026，Vol.24Issue(3)：332-342,11.DOI:10.3969/j.issn.1672-2337.2026.03.010
基于模糊函数多域特征自适应融合的雷达辐射源信号识别
Radar Emitter Signal Recognition Based on Multi-Domain Feature Adaptive Fusion with Ambiguity Function
摘要
Abstract
To address the problems of insufficient feature utilization and poor noise resistance in current radar emit-ter signal recognition methods for complex systems,a recognition method based on multi-domain feature adaptive fusion with ambiguity function is proposed.First,an integral-accelerated ST-NLM algorithm is employed to denoise the signal ambiguity function,and the filtering coefficient is adaptively adjusted through the structure tensor.Then,multi-domain projection features are extracted from the time-delay domain,Doppler frequency domain,and joint time delay-Doppler domain.Finally,a ResNet+adaptive attention feature fusion(AAFF)model is constructed,utilizing multi-scale dilated convolution,depthwise separable convolution,and efficient channel attention(ECA)mechanism to achieve adaptive fea-ture fusion.Experimental results show that the method can maintain 100%accuracy when the signal-to-noise ratio(SNR)is above 0 dB,and even at-4 dB SNR,the recognition rate can still reach 98.12%.It validates that the proposed method has certain effectiveness and feasibility under low SNR conditions.关键词
雷达辐射源信号/模糊函数/多域特征融合/深度学习/注意力机制Key words
radar emitter signal/ambiguity function/multi-domain feature fusion/deep learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
普运伟,李文康,何娅琳,田春瑾..基于模糊函数多域特征自适应融合的雷达辐射源信号识别[J].雷达科学与技术,2026,24(3):332-342,11.基金项目
国家自然科学基金(61561028) （61561028）
昆明理工大学人培基金(KKZ3202403190) （KKZ3202403190）
