Bayesian adaptive filter for tracking with measurements of uncertain origin

The probabilistic data association filter (PDA) estimates the state of a target in the presence of source uncertainty and measurement inaccuracy. This suboptimal procedure assumes that variances of process and measurement noises are known. The aim of this paper concerns the research of an adaptive probabilistic data association filter (APDAF). This Bayesian method estimates the state of a target in a cluttered environment when the noise statistics are unknown. Simulation results on target tracking using experimental data are presented.