基于数据场联合 PRI 变换与聚类的雷达信号分选

摘要

基于脉冲描述字进行雷达信号分选时,传统聚类算法需要预先人工设定聚类中心和聚类数目.针对该问题,提出一种基于数据场理论联合脉冲重复间隔(pulse repetition interval ,PRI)变换与聚类的雷达信号分选新方法.首先,依据数据场理论,基于势值大小实现干扰点剔除,而后利用PRI变换算法进行PRI估计,依据 PRI估计值将归一化脉冲描述字数据预分类,进而以各类数据集中心间的欧氏距离小于辐射因子为准则进行类别合并,自动得到初始聚类中心和聚类数目,最后通过改进K‐M eans算法完成聚类分选.仿真实验表明:所提方法能够应对存在频率捷变,重频参差、抖动、参数交叠、局部脉冲丢失的复杂信号环境,分选正确率明显提升.%In radar signal sorting based on pulse description words (PDW) ,the traditional clustering algorithm needs to set the clustering center and the number of clustering in advance .To solve this problem ,this paper proposes a new radar signal sorting method ,w hich combines pulse repetition interval (PRI) transform and clus‐tering based on the data field theory .Firstly ,noise points are removed based on the potential value according to the data field theory ,and then the PRI transformation algorithm is used to obtain the PRI estimate .Based on the PRI value , the pre‐classification of normalized PDW data are carried out to calculate all kinds of center points ,and the kinds w ith the euclidean distance of its center points less than the radiation factor are combined to determine the initial clustering center and the number of clustering automatically .Finally , the improved K‐means algorithm is used for clustering and sorting .Simulation results show that the proposed method can deal w ith the complex signal environment with frequency agility ,repeated frequency fluctuation ,jitter ,overlapping parameters and partial pulse loss ,and the correct sorting probability is obviously improved .