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

TL;DR

This paper introduces a new tracking algorithm that improves accuracy in tracking multiple groups of targets that split or merge.

Contribution

A novel Belief Propagation-based framework for tracking multiple resolvable group targets with dynamic splitting and merging.

Findings

The proposed algorithm outperforms PMHT, PHD, and JPDA in estimating kinematic states and target cardinality.

Belief Propagation effectively infers association probabilities in a factor graph model.

Minimum Spanning Tree clustering improves measurement partitioning for group targets.

Abstract

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.

Figures