CPHD and PHD filters for unknown backgrounds II: multitarget filtering in dynamic clutter

Abstract

The probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters were introduced in 2000 and 2006, respectively, as approximations of the full multitarget Bayes detection and tracking filter. Both filters are based on the "standard" multitarget measurement model that underlies most multitarget tracking theory. This paper is part of a series of theoretical studies that addresses PHD and CPHD filters for nonstandard multitarget measurement models. In a companion paper I derived the measurement-update equations for CPHD and PHD filters for extracting clusters from dynamically evolving data sets. This paper uses these results to derive CPHD and PHD filters for detecting and tracking multiple targets obscured by unknown, dynamically changing clutter.