Stochastic data association in multi-target filtering
Stefano P. Coraluppi, Craig A. Carthel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Multi-target filtering for closely-spaced targets leads to degraded performance with respect to single-target filtering solutions, due to measurement provenance uncertainty. Soft data association approaches like the probabilistic data association filter (PDAF) suffer track coalescence. Conversely, hard data association approaches like multiplehypothesis tracking (MHT) suffer track repulsion. We introduce the stochastic data association filter (SDAF) that utilizes the PDAF weights in a stochastic, hard data association update step. We find that the SDAF outperforms the PDAF, though it does not match the performance of the MHT solution. We compare as well to the recentlyintroduced equivalence-class MHT (ECMHT) that successfully counters the track repulsion effect. Simulation results are based on the steady-state form of the Ornstein-Uhlenbeck process, allowing for lengthy stochastic realizations with closely-spaced targets.