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  • 郭鹏,牛知艺.基于多域时序解耦注意力网络的雷达信号分选算法[J].电讯技术,2026,(4):613 - 621.    [点击复制]
  • GUO Peng,NIU Zhiyi.Radar Signal Sorting Algorithm Based on Multi-domain Temporal Disentangled Attention Network[J].,2026,(4):613 - 621.   [点击复制]
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基于多域时序解耦注意力网络的雷达信号分选算法
郭鹏,牛知艺
0
(成都华力创通科技有限公司,成都 610095)
摘要:
针对现代电子战中雷达辐射源信号调制方式日益复杂、单一特征域难以有效表征信号本质属性的问题,提出一种基于多域时序解耦注意力网络(Multi-domain Temporal Disentangled Attention Network,MD-TDAN)的雷达信号分选算法。该方法首先在数据预处理阶段构建包含时域正交分量、频域谱分量及瞬时幅相特征的六维全息特征矩阵;随后,设计了多视图解耦注意力模块,利用跨步长自注意力机制捕捉
关键词:  雷达信号分选  多域时序解耦  跨步长自注意力机制  高精度分选
DOI:10.20079/j.issn.1001-893x.251223001
基金项目:国家自然科学基金资助项目(2019YJ0455)
Radar Signal Sorting Algorithm Based on Multi-domain Temporal Disentangled Attention Network
GUO Peng,NIU Zhiyi
(Chengdu Hwa Create Co.,Ltd.,Chengdu 610095,China)
Abstract:
To address the problem that radar emitter signals adopt increasingly complex modulation modes in modern electronic warfare and their essential attributes cannot be effectively characterized by a single feature domain,the Multi-domain Temporal Disentangled Attention Network(MD-TDAN) is proposed as a radar signal sorting algorithm.The method first constructs a six-dimensional holographic feature matrix in the data preprocessing stage.This matrix includes time-domain orthogonal components,frequency-domain spectral components,and instantaneous amplitude-phase features.Subsequently,a multi-view decoupling attention module is designed,utilizing a stride-wise self-attention mechanism to capture the interval patterns of pulse repetition interval(PRI) parameters,effectively addressing the high computational complexity of long sequence signals and the generalization issue of arbitrary PRI adjustments.Finally,the method fuses local texture features and global semantic information through a deep residual convolutional network to achieve high-precision sorting of 8 types of complex system radar signals,such as conventional,staggered,jittered,and sliding signals.Experimental results show that MD-TDAN achieves an average sorting accuracy of 96.25% for 8 types of radar signals,and exhibits excellent robustness and generalization ability in low signal-to-noise ratio and complex modulation environments.
Key words:  radar signal sorting  multi-domain temporal disentanglement  strided self-attention mechanism  high-precision sorting
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