基于PS-IFKCM的弹道中段目标识别方法  

Techniques for target recognition in ballistic midcourse based on particle swarm-based intuitionistic fuzzy kernel c-means

作　　者：余晓东[1] 雷英杰[1] 孟飞翔[1] 雷阳[2] 

机构地区：[1]空军工程大学防空反导学院,陕西西安710051 [2]武警工程大学电子技术系,陕西西安710086

出　　处：《系统工程与电子技术》2015年第1期17-23,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(61272011;61309022);陕西省自然科学基金青年项目(2013JQ8031)资助课题

摘　　要：针对现有直觉模糊核c-均值(intuitionistic fuzzy kernel c-means,IFKCM)聚类算法对初始值敏感、易陷入局部最优解及收敛速度慢等缺陷,汲取了粒子群优化(particle swarm optimization,PSO)算法优势,对初始聚类中心进行优化,提出了基于粒子群优化的直觉核c-均值(particle swarm-based intuitionistic fuzzy kernel c-means,PS-IFKCM)聚类算法,选取4组标准数据集实际样本数据对算法的有效性进行了试验。最后选取弹道中段目标识别常用的雷达截面积(radar cross section,RCS)这一特征属性进行弹道中段目标识别仿真实验,并将其与模糊c-均值(fuzzy c-means,FCM)算法、IFKCM算法的识别效果及运行时间进行比较分析,表明了该算法应用于弹道中段目标识别的有效性及优越性。The intuitionistic fuzzy clustering algorithms are sensitive to the initial value,easy to fall into local opti-mum and have slow convergence speed.To overcome these shortages,the particle swarm optimization（PSO）algorithm with powerful ability of global search and quick convergence rate is applied to intuitionistic fuzzy clustering.Firstly, PSO is used to optimize the initial clustering centers.Then,the approach of intuitionistic fuzzy kernel c-means（IFKCM） based on PSO,namely PS-IFKCM,is proposed.Then,experiments based on four measured datasets are carried out to illustrate the performance of the proposed method.Subsequently,the tactical ballistic missile （TBM）target recognition experiment is carried out based on radar cross section （RCS）,which is usually applied in target rec-ognition in the middle ballistic trajectory.Compared with results from fuzzy c-means and IFKCM,PS-IFKCM is of great efficiency when it comes to target recognition in the middle ballistic trajectory.

关 键 词：直觉模糊集 模糊核c-均值 粒子群优化 弹道中段 目标识别 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程] TP391[自动化与计算机技术—控制科学与工程]