The JPDAF in practical systems: computation and snake oil

Paper in proceeding, 2010

In this paper we look at various options for calculating target-measurement association probabilities and updating the state estimates in the Joint Probabilistic Data Association Filter (JPDAF). In addition to the "standard" methods, we look at other methods that try to improve the estimation accuracy by coupling the states, discarding certain joint association events, or by applying random finite set theory to change how the states are updated. We compare the performance of trackers based on several of these concepts to each other and to the PMHT, the MHT, and the GNN tracker. We also single out approaches that are "snake oil", in that they are either not suited for practical use, or that their complexity is higher than that of calculating the probabilities exactly. Additionally we show how the JPDAF* can be implemented to have a lower worst-case complexity than the regular JPDAF when the number of targets and/or observations is large. We also review some oft overlooked references on gating that are useful for implementations in real systems.