Multiple and Extended Object Tracking with Poisson Spatial Processes and Variable Rate Filters

In this paper we propose a new approach for tracking manoeuvring objects using variable rate particle filters with multiple sensors. Unlike other 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 manoeuvring behaviours of an object. Furthermore, Poisson model is used to model both target and clutter measurements, avoiding the data association step associated with the standard tracking approaches. Computer simulations demonstrate the robust performance of the proposed method for tracking highly manoeuvrable targets in a hostile environment with high clutter density.