雷达辐射源信号分选研究进展  

Progress in Radar Emitter Signal Deinterleaving

作　　者：隋金坪 刘振 刘丽 黎湘[2] SUI Jinping;LIU Zhen;LIU Li;LI Xiang(Operational Software and Simulation Research Institute,Dalian Naval Academy,Dalian 116016,China;College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China;College of System Engineering,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]海军大连舰艇学院作战软件与仿真研究所,大连116016 [2]国防科技大学电子科学学院,长沙410073 [3]国防科技大学系统工程学院,长沙410073

出　　处：《雷达学报（中英文）》2022年第3期418-433,共16页Journal of Radars

基　　金：国家自然科学基金优秀青年科学基金(62022091)。

摘　　要：雷达辐射源信号分选是雷达信号侦察的关键技术之一,同时也是战场态势感知的重要环节。该文系统梳理了雷达辐射源信号分选的主流技术,从基于脉间调制特征、基于脉内调制特征、基于机器学习的雷达辐射源信号分选3个角度阐述了目前雷达辐射源信号分选工作的主要研究方向及进展,并重点阐释了基于深度神经网络、数据流聚类等最新分选技术的原理与特点。最后,对现有雷达辐射源信号分选技术的不足进行了总结并对未来趋势进行了预测。Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness.This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving.It summarizes the main research progress in radar emitter signal deinterleaving from three directions:interpulse modulation characteristics-based,intrapulse modulation characteristics-based,and machine learning-based research.Particularly,this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology,such as neural network-based and data stream clustering-based techniques.Finally,the shortcomings of the current radar emitter deinterleaving technology are summarized,and the future trend is predicted.

关 键 词：雷达辐射源 脉冲分选 脉冲流 机器学习 深度学习 数据流聚类 

分 类 号：TN911.7[电子电信—通信与信息系统]