Unified sensor management using CPHD filters

The PHD filter propagates a multitarget statistical first moment, the probability hypothesis density (PHD), in place of the full multitarget posterior distribution. It has been the basis of a systematic approach to multisensor, multitarget sensor management based on the posterior expected number of targets (PENT) objective function. The PHD filter has since been generalized to the cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the full probability distribution on target number. The CPHD filter provides more accurate estimates of target number and target states. This paper shows how PENT-based objective functions can be naturally extended for use with the CPHD filter.