Multiple and extended object tracking with Poisson spatial processes and variable rate filters

Abstract

In this paper we propose methods for tracking multiple maneuvering objects using variable rate particle filters with multiple sensors. Unlike more standard approaches the proposed method assumes that the states change at different and unknown rates compared with the observation process, and hence is able to model parsimoniously the maneuvering behaviour of an object. Furthermore, a Poisson model is used to model both target and clutter measurements, avoiding the data association difficulties associated with traditional tracking approaches. Computer simulations demonstrate the potential of the proposed method for tracking highly maneuverable targets in a hostile environment with high clutter density.