An alternative form of cardinalized PHD filter or I.I.D.-approximation filter

In this paper, we derive the updating formula of the cardinalized probability hypothesis density (CPHD) filter recently developed in the works of Mahler et al., (2006) from the non- Poisson multiple-hypothesis tracking (MHT) algorithm developed earlier in the works of Mori et al. (2004). The particular form of the CPHD updating formula developed in this paper is expressed only with the probability hypothesis density (PHD) or the a posteriori intensity measure density and the a posteriori probability of the number of targets, without using the probability generating function, and is consistent with the updating formula in the work of Ba-Toung Vo et al. (2007). Several issues concerning the CPHD updating formula and the sensor modeling are discussed together with a couple of very simple but illustrative examples.