Multipath Generalized Labeled Multi-Bernoulli Filter
Bin Yang, Jun Wang, Wenguang Wang, Shaoming Wei · 2018
Traditional multitarget tracking algorithms assume that each target can generate at most one detection per scan. However, in the over-the-horizon radar (OTHR), a target may produce multiple detections because of multipath propagation. In this paper, we propose a new algorithm, called multipath generalized labeled multi-Bernoulli (MP-GLMB) filter, to effectively track multiple targets in such multiple-detection systems. The proposed technique is based on the labeled random finite set (RFS), which estimates the number of targets and the trajectories of their states. The proposed MP-GLMB filter is compared with the multipath version of the probability hypothesis density (PHD) filter and the multi-target multi-Bernoulli (MeMber) filter, and simulation results show that our algorithm has improved tracking performance.