Belief propagation for marginal probabilities in multiple hypothesis tracking
Odin A. Severinsen, Lars-Christian Ness Tokle, Edmund Brekke · 2023
This paper explores evaluation of association marginals in multiple hypothesis tracking. The work builds upon recent results where loop belief propagation (LBP) has been used in single-hypothesis cases. There are two contributions in the paper. The first is a novel factor graph representation of the joint multi-hypothesis association posterior. The second contribution is two algorithms that both use LBP to evaluate association marginals. The first method uses total probability in conjunction with hypothesis-conditioned LBP, and is called PHD-LBP. The second method is an LBP algorithm running directly on the full multi-hypothesis association graph with novel, specialized message definitions that are derived in this paper and efficient to compute and store in memory, and is called MH-LBP. Results show that both algorithms perform well with high correlation with the exact marginals for the majority of the cases.