Path sampling for particle filters with application to multi-target tracking

In recent work [15], we have presented a novel approach for improving particle filters for multi-target tracking. The suggested approach was based on Girsanov’s change of measure theorem for stochastic differential equations. Girsanov’s theorem was used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations. In the current work, we present an alternative way to append a Markov Chain Monte Carlo step to a particle filter to bring the particle filter samples closer to the observations. Both current and previous approaches stem from the general formulation of the filtering problem. We have used the currently proposed approach on the problem of multi-target tracking for both linear and nonlinear observation models. The numerical results show that the suggested approach can improve significantly the performance of a particle filter. Introduction Multi-target tracking is a central and difficult problem arising in many scientific and engineering applications including radar and signal processing, air traffic control and GPS navigation [12]. The tracking problem consists of computing the best estimate of the targets’ trajectories based on noisy