摘要
针对现代认知电子侦察方法中雷达系统部署多个信号源和雷达对抗措施而产生的复杂电磁环境,严重限制了获取有效目标识别所需的先验信息程度问题。本文提出了一种基于雷达信号的双视角协同聚类方法对辐射源进行分类,特别应用于双视角的场景下。所提方法也是从双视角的场景下,让两个信号视角获得的聚类结果之间差异,通过线性判别分析迭代地执行无监督聚类、聚类标签转移和降维,使得辐射信号排序可以在非协同环境中进行。实验验证:所提方法可以充分利用基本信号特征与脉内特征之间的差异信息,提高基于聚类的辐射源分选的精度。因此,所提方法的排序能力具有较高的实际价值。
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.
作者
吴小丹
黄朝围
王剑
狄慧
谷晓鹰
WU Xiaodan;HUANG Chaowei;WANG Jian;DI Hui;GU Xiaoying(School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University,Shanghai 200240,China;Shanghai Aerospace Electronic Technology Institute,Shanghai 201109,China;Shanghai Institute of Satellite Engineering,Shanghai 201109,China)
出处
《上海航天(中英文)》
2025年第1期186-196,共11页
Aerospace Shanghai(Chinese&English)
基金
国家自然科学基金资助项目(62071291)。
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)
radar feature spectrum
dual-view coordinated clustering
radar signal
dual-spectral characteristics
kernel principal component analysis(KPCA)
