On Tracking Applications using Variable Rate Particle Filters

Abstract

In this paper we propose an online tracking algorithm for multiple manoeuvring targets using variable rate particle filters (VRPFs). Unlike conventional particle filters, VRPFs combined with an intrinsic dynamical model enables us to track the manoeuvring behaviour of an object even though only a single dynamical model is employed. Furthermore a Markov Random Field motion model is included for modelling target interactions. In this paper we propose to integrate a data-dependent importance sampling method with the framework to generate more representative state particles. A Poisson observation model is also used to model both targets and clutter measurements, avoiding the data association difficulties associated with traditional tracking approaches. Finally computer simulations demonstrate the potential of the proposed method for tracking multiple highly manoeuvrable targets in a hostile environment with high clutter density and low detection probability.