Modified Joint Probability Data Association Algorithm Controlling Track Coalescence
Joint Probabilistic Data Association has been proven to be effective in tracking multiple targets from measurements amidst clutter and missed detections. But the traditional Joint Probabilistic Data Association algorithm will cause track coalescence when the targets are parallel neighboring or smallangle crossing. To avoid track coalescence, a modified Joint Probabilistic Data Association algorithm is proposed in this paper. An exclusive measurement is defined for every target in the new algorithm, and an arbitrary positive scaling factor will be employed to multiply the maximum probabilities of every target associated with measurements. At last, the Entropy Value Method will be used twice to give weights to the association probabilities of every measurement. The simulation results show that the new algorithm can effectively avoid track coalescence in all kinds of scenarios and its performance is better than the track performance when the Entropy Value Method is used only one time.
Joint Probabilistic Data Association track coalescence exclusive measurement Entropy Value Method
Yibing Xu Songlin Chen Zhaohui Wang Lianrui Kang
Xian Communications Institute, Xian, Shanxi, 710106, China
国际会议
深圳
英文
442-445
2011-03-28（万方平台首次上网日期，不代表论文的发表时间）