Parallel Particle Filters for Multiple Target Tracking

The Multiple Targets Tracking (MTT) problem is addressed in signal and image processing. When the state and measurement models are linear, we can find several algorithms that yield good performances in MTT problem, among them, the Multiple Hypotheses Tracker (MHT) and the Joint Probabilistic Data Association Filter (JPDAF). However, if the state and measurement models are nonlinear, these algorithms break down. In this paper we propose a method based on particle filters bank, where the objective is to make a contribution for estimating the trajectories of several targets usi ng only bearings measurements. The main idea of this algorithm is to combine the Multiple Model approach (MM) with Sequential Monte Carlo methods (SMC). The result from this combination is a Nonlinear Multiple Model Particle Filters algorithm (NMMPF) able to estimate the trajectories of multiple targets.