  This paper is concerned with the problem of tracking single or multiple
targets with multiple non-target specific observations (measurements). For such
filtering problems with data association uncertainty, a novel feedback
control-based particle filter algorithm is introduced. The algorithm is
referred to as the probabilistic data association-feedback particle filter
(PDA-FPF). The proposed filter is shown to represent a generalization to the
nonlinear non-Gaussian case of the classical Kalman filter-based probabilistic
data association filter (PDAF). One remarkable conclusion is that the proposed
PDA-FPF algorithm retains the innovation error-based feedback structure of the
classical PDAF algorithm, even in the nonlinear non-Gaussian case. The
theoretical results are illustrated with the aid of numerical examples
motivated by multiple target tracking applications.
