An alternative derivation of a Bayes tracking filter based on finite mixture models

Ba-Tuong-Vo et al proposed a Bayes filter of single target in the random finite set framework. In this paper, we first extend the parameter mixture models (PMM) to state mixture models(s). And further an alternative derivation of a Bayesian tracking filter in clutter is proposed for single target. The key of the proposed algorithm is to derive the measurement likelihood function based on finite mixture models. In addition, a closed-form recursion under the linear Gaussian assumption is discussed.