A Bayesian approach to tracking multiple targets using sensor arrays and particle filters

Abstract

We present a Bayesian approach to tracking the direction-of-arrival (DOA) of multiple moving targets using a passive sensor array. The prior is a description of the dynamic behavior we expect for the targets which is modeled as constant velocity motion with a Gaussian disturbance acting on the target's heading direction. The likelihood function is arrived at by defining an uninformative prior for both the signals and noise variance and removing these parameters from the problem by marginalization. Recent advances in sequential Monte Carlo (SMC) techniques, specifically the particle filter algorithm, allow us to model and track the posterior distribution defined by the Bayesian model using a collection of target states that can be viewed as samples from the posterior of interest. We describe two versions of this algorithm and finally present results obtained using synthetic data.