The multiple detection joint integrated track splitting filter
Yuan Huang, Taek Lyul Song, Chul Mok Lee · 2016
The point target assumption which allows each target to generate at most one measurement at each scan is widely used in target tracking field. However, in come tracking scenarios, one target can generate many measurements due to high sensor resolution which give rise to the multiple detection problem. Traditional algorithms get poor performances since each data association event considers only one measurement as target detection to estimate target state. The measurement partition method which forms all possible combinations of target originated measurements is designed for multiple detection problem. In this paper, joint integrated track splitting (JITS) tracker and measurement partition method are combined to generate a new structure, called multiple detection joint integrated track splitting (MD-JITS) tracker, to extract and utilize the target motion information contained in the measurements more effectively. Compared with some existing filters, this filter achieves better tracking performance for automatic false track discrimination (FTD).