On the Convergence of Belief Propagation for Multipath Data Association in Target Tracking
For target tracking researchers, this fills a theoretical gap in convergence guarantees for multipath data association, though the practical impact is incremental.
The paper provides the first convergence proof for belief propagation in multipath data association, establishing convergence to a unique fixed point. Simulations show favorable accuracy-efficiency trade-off compared to existing trackers.
Belief propagation (BP) is widely used for data association (DA) in target tracking. Existing convergence analyses of BP for DA address only the two-way correspondence between targets and measurements, where each target generates at most one measurement per scan. Multipath DA (MPDA) allows a single target to produce multiple measurements via distinct propagation paths, creating a three-way correspondence among targets, paths, and measurements, for which a complete convergence proof has not yet been provided. We provide such a proof for the BP updates in MPDA, establishing convergence to a unique fixed point. Simulations illustrate the convergence behavior of BP in MPDA and demonstrate a favorable accuracy--efficiency trade-off relative to both single-scan and two-scan variants of the multiple-detection multiple-hypothesis tracker.