Particle Filters for Multiple Target Tracking

Abstract

Multiple target tracking has immense application in areas such as surveillance, air traffic control, defense and computer vision. The aim of a target tracking algorithm is to estimate the target position precisely from the partial noisy observations available. The real challenges of multiple target tracking are to accomplish the same in the presence of measurement origin uncertainty and clutter. Optimal solutions are available by way of Kalman filters for the special case of linear dynamical systems with Gaussian noise. For a more general scenario, we resort to the suboptimal solutions like Particle filters which implement stochastic filtering through a sequential Monte Carlo approach. Measurement origin uncertainty is resolved by using a suitable data association technique prior to the filtering. This paper explores the possibilities of applying a variant of Ensemble Square Root Filters (EnSRF) in a multiple target tracking scenario and its tracking performance is compared with those of conventional Bootstrap and Auxiliary Bootstrap particle filters. The filtering scheme proposed here incorporates Sample based Joint Probabilistic Data Association (SJPDA) in the EnSRF framework for dealing with measurement origin uncertainty.