Resolvable Group State Estimation with Maneuver Movement Based on Labeled RFS
Yudong Chi, Weifeng Liu · 2018
This paper considers the problem of tracking multiple resolvable group targets using the labeled random finite set framework. While the generalized labeled multi-Bernoulli (GLMB) filter is an efficient multi-target tracking filter, it cannot capture the the dependence or correlation between members of each group. In this paper, we introduce a group target model by incorporating graph theory into the labeled random finite set framework, which accounts for dependence between group members. We then propose a GLMB approximation of the prediction and update step of the Bayes filter for multiple resolvable group targets. Simulation are presented to benchmark the proposed filter against the GLMB filter.