Second-generation PHD/CPHD filters and multitarget calculus
Ronald Mahler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
The "classical" PHD and CPHD filters presume the standard "small-target" detection model. This year, in a series of theoretical studies, I have derived new "second-generation" CPHD/PHD filters for various sensing conditions that cannot be described by the standard model. These are: (a) multisensor, (b) clutter estimation, (c) tracking in unknown clutter, (d) extended targets, (e) unresolved targets, and (f) superpositional sensors. A common factor underlies all of these derivations: the FISST multitarget calculus. It is possible, given that one already knows the correct "answer," to reverse engineer the classical PHD/CPHD filters and to extemporize some "elementary" means of deriving them. But only the multitarget calculus is guaranteed to result in theoretically rigorous formulas for new problems-i.e., those for which the answer is not known beforehand. I also announce an important new result: the multitarget state estimators used with the CPHD/PHD filters are Bayes-optimal.