CPHD filters for superpositional sensors
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 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. That is, sensor measurements are presumed to be detections. Other sensors collect measurements that are not detections, and among the most important of these are superpositional sensors. A measurement collected by such a sensor is a sum of the real- or complex-valued signals generated by an unknown number of unknown targets present in the scene. This paper describes a theoretical extension of the CPHD filter concept to superpositional sensors.