Double-threshold technique-based tracks association in multisensor-multitarget tracking

A great deal of attention is currently focused on multi-sensor track fusion (TF), for it can not only achieve improved performance and provide more inferences, but also reduce the computational complexity and the bandwidth of transmission. This paper makes use of Joint Probabilistic Data Association (JPDA) algorithm to track the multi-target for each sensor in cluttered environment. And one of the most important aspects of multi-sensor track fusion is track-to-track-association (TTTA). To solve the problem, an approach of double-threshold tracks association is presented. It uses the track weighted fusion approach to fuse each track from sensors. Compared to a single sensor´s performance, Simulation results show that the proposed approach achieves considerable performance improvement for multisensor-multitarget tracking in cluttered environment.