  This paper introduces a novel feedback-control based particle filter for the
solution of the filtering problem with data association uncertainty. The
particle filter is referred to as the joint probabilistic data
association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the
feedback particle filter introduced in our earlier papers. The remarkable
conclusion of our paper is that the JPDA-FPF algorithm retains the innovation
error-based feedback structure of the feedback particle filter, even with data
association uncertainty in the general nonlinear case. The theoretical results
are illustrated with the aid of two numerical example problems drawn from
multiple target tracking applications.
