Multiple Resolvable Group Estimation Based on the GLMB Filter with Graph Structure
Weifeng Liu, Yudong Chi, Guilin Zhang · 2018
In this paper, we focus on multiple resolvable group target tracking in the framework of labeled random finite sets. The original GLMB cannot be used for multiple resolvable group tracking due to the dependence of targets in the group. We describe the collaboration noise. random finite set and then given the state predict and update for a resolvable group target. Further, the δ-GLMB filter recursion for the resolvable group targets is proposed. Our research shows that the original GLMB filter can be improved and give a better results. A simulation is provided to verify the proposed algorithm.