Multiple Target Tracking with Constrained Motion Using Particle Filtering Methods

Abstract

We propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function.