数据采集与处理2017，Vol.32Issue(1)：78-85,8.DOI:10.16337/j.1004-9037.2017.01.009
基于观测数据聚类划分的扩展目标跟踪算法
Extended Target Tracking with Clustering of Measurement Partitioning
摘要
Abstract
A novel K-means algorithm of measurement partitioning is proposed to overcome the problem of distance partitioning algorithm in Gaussian mixture probability hypothesis density filter for extended target tracking.The number of the targets is estimated by maximum-likelihood estimator and then the estimates of the target number are used as the cluster number of K-means.An elliptical gate is introduced to remove the clutter measurements for depressing the influence of clusters.Simulation results show that the proposed algorithm reduces the computational complexity obviously,and obtains an improved performance.关键词
多目标跟踪/扩展目标跟踪/概率假设密度滤波/观测集合划分/K-means聚类Key words
multiple-target tracking/extended target tracking/GM-PHD filter/measurement partitioning/K-means clustering分类
信息技术与安全科学引用本文复制引用
章涛,来燃,吴仁彪..基于观测数据聚类划分的扩展目标跟踪算法[J].数据采集与处理,2017,32(1):78-85,8.基金项目
国家自然科学基金(61471365,61231017,61571442)资助项目 （61471365,61231017,61571442）
中国民航大学中央高校基金(3122015D003)资助项目. （3122015D003）
