Approximate multisensor CPHD and PHD filters

Abstract

The probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter are principled approximations of the general multitarget Bayes recursive filter. Both filters are single-sensor filters. Since their multisensor generalizations are computationally intractable, a further approximation - iterating their corrector equations, once for each sensor - has been used instead. This approach is theoretically unpleasing because it is not invariant under reordering of the sensors, and because it is implicitly based on strong simplifying assumptions. The purpose of this paper is to derive multisensor PHD and CPHD filters that (1) are invariant under sensor reordering, (2) require much weaker simplifying assumptions, and (3) are potentially computationally tractable (at least in the case of the multisensor CPHD filter).