电讯技术2025，Vol.65Issue(10)：1587-1594,8.DOI:10.20079/j.issn.1001-893x.240618001
面向雷达信号预分选的粒子群快速密度聚类算法
A Particle Swarm Fast Density Clustering Algorithm for Radar Signal Pre-sorting
摘要
Abstract
To efficiently sort dense and overlapping radar pulse signals in dynamic electronic warfare environments,a particle swarm fast density clustering algorithm(PSK-DBSCAN)is proposed.This algorithm addresses the limitations of existing density-based spatial clustering of applications with noise(DBSCAN)radar signal sorting methods,such as susceptibility to interference,reliance on manual parameter tuning and high computational complexity.The algorithm first applies data field theory to remove interference points in radar pulse signals,enhancing sorting precision.Then,it incorporates particle swarm optimization with a silhouette coefficient-based fitness function to adaptively determine optimal clustering parameters.Finally,by integrating a K-Dimensional Tree(K-D Tree),the algorithm significantly reduces the computational complexity of DBSCAN,decreasing sorting time.Experimental results demonstrate that PSK-DBSCAN achieves a sorting accuracy of 98.9%and exhibits robust performance with complex radar signal patterns.关键词
雷达信号分选/数据场/粒子群算法/K-D树/密度聚类Key words
radar signal sorting/data field/particle swarm optimization/K-D tree/density clustering分类
信息技术与安全科学引用本文复制引用
路心雨,黄永辉,崔天舒,朱岩,韩佳宝..面向雷达信号预分选的粒子群快速密度聚类算法[J].电讯技术,2025,65(10):1587-1594,8.基金项目
中国科学院国防重点实验室基金 （）
