Rao-Blackwellized Monte Carlo Data Association for Multiple Target Tracking

Abstract

We propose a new Rao-Blackwellized sequential Monte Carlo method for tracking multiple targets in presence of clutter and false alarm measurements. The advantage of the new approach is that Rao-Blackwellization allows the estimation algorithm to be partitioned into single target tracking and data association sub-problems, where the single target tracking sub-problem can be solved by Kalman filters or extended Kalman filters, and the data association by sequential importance resampling. Because the sampled sub-space is finite, it is possible to use the optimal importance distribution explicitly, which significantly reduces the required number of Monte Carlo samples.