摘要
针对多目标跟踪中的数据关联问题,提出一种基于类云模型c-均值聚类的数据关联算法.该算法采用类云模型c-均值聚类算法对目标有效回波进行聚类,将聚类中心作为目标最终观测值,运用最近邻法对聚类中心与航迹进行关联,用Kalman滤波器进行状态估计.实验结果表明,本算法与联合概率数据互联算法相比,跟踪精度高,计算量小,更适应于工程应用.
To solve the data association of multi-target tracking, a novel algorithm of data association was proposed based on cloud c-means clustering （CCM）. The CCM algorithm was used to cluster effective echoes, and the resulting cluster centers were considered the final measurements of the targets. The nearest neighboring algorithm was used to associate the cluster centers with the tracks, and the Kalman filter was employed for state estimation. Results from the experiment show that the proposed algorithm has a better tracking accuracy and a lower computational load than the joint probabilistic data association algorithm, and it is more convenient for engineering applications.
出处
《深圳大学学报（理工版）》
EI
CAS
北大核心
2010年第1期11-15,共5页
Journal of Shenzhen University(Science and Engineering)
基金
武器装备预研基金资助项目(9140C***C80)
广东省自然科学基金资助项目(8451806001001836)
关键词
信息处理技术
数据关联
聚类云模型
云c-均值聚类
最近邻算法
多目标跟踪系统
information processing technology
muhitarget tracking
data association
cluster cloud model
cloud c-means clustering
nearest neighbor algorithm
multitarget tracking system
