Abstract:

According to the theory of finite set statistics, a cardinalized probability hypothesis density (CPHD) filter is proposed for tracking unresolved targets. Similar to the original point-target CPHD filter, the proposed unresolved-target CPHD filter propagates not only the first-order statistical moment but also the entire probability distribution on the unresolved-target number. Monte Carlo imulation results show that the target number and state estimation from the proposed unresolved-target CPHD filter are more accurate and reliable than those of Mahler’s unresolved-target PHD filter although the computational load of the proposed CPHD filter is more expensive than that of the unresolved-target PHD filter.