Online Multisensor-Multitarget Detection and Tracking Using Variable Rate Particle Filters

Abstract

In this paper we present an online approach for joint detection and tracking for multiple targets using variable rate particle filters (VRPFs). Unlike conventional models and particle filters, the proposed method utilises the applied forces (tangential and radial components) to model target motions and does not assume the hidden state to change at the same rate as the observations. In effect not only does the proposed method enable us to model parsimoniously the manoeuvring behaviours of targets with a single dynamical model but it also provides a more efficient framework for recursive estimation of the targets' positions since much fewer states will be estimated. In addition, a target detection/termination module will be integrated in the proposed method in which a track initiation, termination, or maintenance move is randomly executed using Bayesian Monte Carlo methods. To model a more realistic observation environment Poisson process is chosen for all target originating and spurious measurements. Unlike other observation models, the proposed model does not require extensive computation for data association between active targets and observations, prior to target state estimation, as a result. To improve the quality of the particles we adopt a data-dependent importance sampling strategy in which the latest observations are involved when new particles are updated. This enables the target states to be updated as new observations arrive while keeping the number of states sufficiently low to track the manoeuvres of the targets. Computer simulations demonstrate the potential of the proposed method for detecting and tracking multiple highly manoeuverable targets in a hostile environment with high clutter density and low detection probability.