A Novel Belief Propagation-Based Probabilistic Multiple Hypothesis Tracking Algorithm for Multiple Resolvable Group Targets

A key challenge in multiple group target tracking is to maintain consistent data association in the presence of dynamic evolutions, i.e., splitting and merging. This paper proposes a Belief Propagation-based Multiple Hypothesis Tracking framework. The measurements are partitioned by using the Minimum Spanning Tree divisive clustering algorithm. A factor graph model is then constructed for the association hypotheses between group targets and measurements, followed by the inference of marginal posterior association probabilities via the Belief Propagation. These probabilities are finally integrated into an Expectation-Maximization framework, and the group states are updated by maximizing the expected log-likelihood function. Simulation results demonstrate that the proposed algorithm achieves significantly higher accuracy in the joint estimation of kinematic states and target cardinality compared to the PMHT-based, PHD-based, and JPDA-based algorithms.