Tracking correlated, simultaneously evolving target populations, Ⅱ

摘要

This paper is the sixth in a series aimed at weakening the independence assumptions that are typically presumed in multitarget tracking. Earlier papers investigated Bayes filters that propagate the correlations between two evolving multitarget systems. Last year at this conference we attempted to derive PHD filter-type approximations that account for both spatial correlation and cardinality correlation (i.e., correlation between the target numbers of the two systems). Unfortunately, this approach required heuristic models of both clutter and target appearance in order to incorporate both spatial and cardinality correlation. This paper describes a fully rigorous approach- provided, however, that spatial correlation between the two populations is ignored and only their cardinality correlations are taken into account. We derive the time-update and measurement-update equations for a CPHD filter describing the evolution of such correlated multitarget populations.