基于CODHD聚类划分的多扩展目标跟踪算法  

Multiple extended target tracking algorithm based on CODHD clustering division

作　　者：苗露 李鸿艳[1] 冯新喜[1] MIAO Lu;LI Hongyan;FENG Xinxi(Information and Navigation College,Air Force Engineering University,Xi’an 710077,China)

机构地区：[1]空军工程大学信息与导航学院,西安710077

出　　处：《计算机工程与应用》2018年第19期261-265,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61571458)

摘　　要：杂波环境下,利用概率假设密度滤波器进行扩展目标跟踪存在量测集划分难且计算效率低的问题,提出基于层次划分密度的聚类优化(CODHD)算法对扩展目标进行量测集划分的方法。先利用自适应椭球门限的方法对量测集进行预处理,通过簇合并方式生成量测划分;计算各划分聚类质量并构造为质量曲线;将得到的聚类数和聚类中心通过模糊C-均值(FCM)运算获得量测划分。仿真结果表明,利用所提方法对量测集进行划分,能够得到准确的划分结果且计算代价得到降低。In the background of clutter,the Probability Hypothesis Density(PHD)filter is used to carry out the extended target tracking where the measurement set is difficult to partition and the computational efficiency is low.A method is proposed to divide the measurements for extended target by using the Clusters Optimization based on Density of Hierarchical Division(CODHD)clustering algorithm.Firstly,the adaptive ellipsoid threshold method is used to pre-process the measurement set,the measurement division is generated by using the clusters merging method.The clustering quality for each partition are calculated so as to build the quality curve,and measurement division is obtained through calculating the gained clusters number and clustering center by Fuzzy C-Means(FCM)operation.The simulation results have shown that the method can be used to divide the measurement set while the result of accurate partitioning can be obtained,and the cost of the calculation is obviously reduced.

关 键 词：扩展目标跟踪 量测集划分 层次划分 椭球门限 模糊C-均值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]