上海航天（中英文）2025，Vol.42Issue(1)：186-196,11.DOI:10.19328/j.cnki.2096-8655.2025.01.020
基于双视角协同聚类和特征谱的雷达辐射源分类
Radar Radiation Source Classification Based on Dual-View Collaborative Clustering and Feature Spectra
摘要
Abstract
The complex electromagnetic environment generated by the deployment of multiple signal sources and radar countermeasures in modern cognitive electronic surveillance methods severely limits the degree of prior information available for effective target identification.In this paper,a dual-view collaborative clustering method based on radar signals is proposed to classify radiation sources,especially in dual-view scenarios.The proposed method iteratively performs unsupervised clustering,cluster label transfer,and dimension reduction through linear discriminant analyses,by which the differences between the clustering results obtained from dual-view scenarios can be distinguished,enabling radiation signal ranking in non-cooperative environments.The experimental results demonstrate that the proposed method can effectively leverage the differences between the basic signal features and intra-pulse characteristics,and enhance the accuracy of cluster-based radiation source sorting.Therefore,the sorting ability of the proposed method has very high practical value.关键词
雷达特征谱/双视角协调聚类/雷达信号/双光谱特性/核主成分分析(KPCA)Key words
radar feature spectrum/dual-view coordinated clustering/radar signal/dual-spectral characteristics/kernel principal component analysis(KPCA)分类
电子信息工程引用本文复制引用
吴小丹,黄朝围,王剑,狄慧,谷晓鹰..基于双视角协同聚类和特征谱的雷达辐射源分类[J].上海航天（中英文）,2025,42(1):186-196,11.基金项目
国家自然科学基金资助项目(62071291) （62071291）
