摘要
针对传统雷达信号识别方法对重点目标识别的针对性、时效性不强的问题,提出一种基于聚类和时序相关的重点雷达信号实时识别方法。首先,依据具有噪声的基于密度的聚类(density-based spatial clustering of application with noise,DBSCAN)算法对侦获信号的脉冲描述字进行分选;而后,利用分选所得脉冲的时序特征与重点目标信号脉冲重复间隔(pulse repetition interval,PRI)生成仿真信号;最后,计算仿真信号的互相关函数,基于相关度判断PRI参数是否匹配。仿真实验表明:所提方法明显提升了对重点目标信号的识别时效,能够应对存在噪声干扰和信号交叠的复杂信号环境,对局部脉冲参数丢失不敏感。
Aiming at the pertinence and ineffectiveness of the traditional radar signal recognition method in identifying key targets,a real-time radar signal recognition method based on clustering and time-series correlation is proposed.Firstly,pulse description words of detected signals are sorted based on density-based spatial clustering of the application with noise(DBSCAN)algorithm.Then,the timing characteristics of the sorting pulse and the pulse repetition interval(PRI)parameters of the key target signal are used to generate the simulation signal.Finally,the cross-correlation function of the simulated signal is calculated,and the PRI parameter is judged to be matched based on the degree of correlation.Simulation results show that the proposed method significantly improves the identification time of key target signals,can deal with the complex signal environment with noise interference and overlapping signals,and is not sensitive to the loss of local pulse parameters.
作者
张怡霄
郭文普
康凯
姚云龙
王攀
ZHANG Yixiao;GUO Wenpu;KANG Kai;YAO Yunlong;WANG Pan(Department of Operational Support,Rocket Force University of Engineering,Xi’an 710025,China;Unit 96816 of the PLA,Jinhua 322100,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2020年第3期597-602,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(61501469)资助课题
关键词
雷达信号识别
基于密度的具有噪声的聚类算法
脉冲描述字
时序相关
radar signal recognition
density-based spatial clustering of application with noise(DBSCAN)algorithm
pulse description words
time-series correlation
