摘要
在复杂体制雷达共存的高密度信号环境中,传统基于脉冲描述字的雷达辐射源信号分选识别方法通常直接将一个或多个脉间特征向量进行建模分类,难以满足高脉冲丢失率情况下现代电子战的要求,且缺乏对脉冲间相互作用关系的拓扑结构建立有效的表征和分析模型。提出一种基于脉间参数的复杂网络建模及其分解的局部特征提取方法,利用有限可穿越视图构建复杂网络并借助其结构特性来获取全脉冲序列脉间参数的变化规律,采用对网络局部变化敏感的微观特性表征整个网络模型。通过对截获的雷达辐射源信号进行仿真验证,提取的特征具有良好的可分性,与常规五参数分类结果进行比较和分析表明,所提方法能有效提高雷达辐射源信号识别的正确率。
In the high-density signal environment where advanced radar coexists,the traditional radar signal sorting and recognition method based on pulse descriptive word(PDW)usually performs mod-eling classification which is difficult to meet the demands of modern electronic warfare under the high pulse loss rate,and lack an effective characterization and analysis model to establish the topological struc-ture of the interaction between pulses.Therefore,in this paper,a new method is proposed to extract the local characteristics of complex network modeling and its decomposition approach based on inter-pulse parameters.Meanwhile,the algorithm of limited penetrable visibility graph(LPVG)is used to construct complex network and the structural characteristics of complex network to obtain the regularity of inter-pulse parameters of the whole pulse sequence,and characterizes the whole network model by the micro-characteristics sensitive to local changes in the network.The simulated results of the intercepted unknown radar emitter signals demonstrate that the extracted features have good separability.Comparisons and analyses with conventional five-parameter classification results show that this proposed method can effec-tively improve the identification accuracy of radar emitter signals.
作者
陈韬伟
王会源
马一鸣
刘建业
段正泰
CHEN Taowei;WANG Huiyuan;MA Yiming;LIU Jianye;DUAN Zhengtai(School of Information&lnstitute of Intelligent Applications,Yunnan University of Finance and Economics,Kunming 650221,China;Information Center,Yunnan University of Finance and Economics,Kunming 650221,China)
出处
《火力与指挥控制》
CSCD
北大核心
2024年第12期77-84,92,共9页
Fire Control & Command Control
基金
国家自然科学基金资助项目(61961042,61461051)。
关键词
雷达辐射源信号
脉冲描述字
复杂网络
有限可穿越视图
特征提取
radar emitter signal
pulse descriptive word
complex network
limited penetrability visibility graph
feature extraction
作者简介
陈韬伟(1972-),男,江苏昆山人,博士。
