CPHD and PHD filters for unknown backgrounds I: dynamic data clustering
Ronald Mahler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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 this paper I derive the measurement-update equations for CPHD and PHD filters that estimate models of unknown, dynamically changing data, such as background clutter. A companion paper generalizes these results to multitarget detection and tracking in unknown, dynamic clutter.