Modified joint probabilistic data association with classification-aided for multitarget tracking |
| |
Authors: | Ba Hongxin Cao Lei He Xinyi Cheng Qun |
| |
Affiliation: | 1. Air Force Command Coll., Beijing 100097, P. R. China 2. Inst. of Command Automation, PLA Univ. of Science & Technology, Nanjing 210007, P. R. China 3. Inst. of Combat Systems, Naval Academy of Armament, Beijing 100073, P. R. China 4. Scientific Research Dept., PLA Univ. of Science & Technology, Nanjing 210007, P. R. China |
| |
Abstract: | Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid. |
| |
Keywords: | multi-target tracking data association joint probabilistic data association classification information track coalescence maneuvering target |
本文献已被 维普 万方数据 ScienceDirect 等数据库收录! |
|