Track Association for Dynamic Target Tracking System Based on AP Algorithm  

作　　者：储岳中 徐波 高有涛 

机构地区：[1]College of Astronautics,Nanjing University of Aeronautics and Astronautics [2]School of Computer Science,Anhui University of Technology [3]School of Astronomy & Space Science,Nanjing University

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2014年第6期643-651,共9页南京航空航天大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(11078001)

摘　　要：Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system, and its accuracy directly impacts on the performance of the whole tracking system. A multi-sensor data association is proposed based on aftinity propagation （AP） algorithm. The proposed method needs an initial similarity, a distance between any two points, as a parameter, therefore, the similarity matrix is calculated by track position, velocity and azimuth of track data. The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index. Furthermore, the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target. Finally, the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics, then filtering and target tracking are conducted. The experimental results show that the proposed method can ef- fectively achieve multi-sensor and multi-target track association.

关 键 词：affinity propagation algorithm data fusion target tracking track association 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]