A hybrid bootstrap filter for target tracking in clutter

The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered. A new hybrid bootstrap filter is proposed. The bootstrap filter is an approach where random samples are used to represent the target posterior distributions. By using this approach, the author circumvents the usual problem of an exponentially increasing number of association hypotheses as well as allowing the use of any nonlinear/non-Gaussian system and/or measurement models.