Variable rate particle filters for tracking applications

Here we describe recent advances in particle filtering algorithms and models for tracking of manoeuvring objects in clutter. The methods develop on the basic variable dimension particle filtering algorithms introduced in [1], in which a new type of dynamical model is introduced whose state variables arrive at unknown times relative to the observation process (hence 'variable rate'). Targets are assumed to follow deterministic trajectories in between state times, determined by an appropriate model, such as the differential equation model for the object. The framework allows for automatic modelling and estimation of the trajectories of targets using an adaptation of particle filtering methods [2] into the variable dimension setting. In this paper we introduce more effective sampling schemes for the variable rate setting that ensure future states are only generated as and when required, new dynamical models appropriate for manoeuvring objects, and new observation models under the assumption of a non-homogeneous Poisson process for both targets and clutter. Simulations show very effective tracking performance under challenging settings which cannot be emulated in a standard fixed rate scheme.