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The Integrated Probabilistic Data Association (IPDA) type filters provide estimates of the underlying target probability of existence as well as they track state maintenance. For each scan, IPDA recursive calculates the probability of target existence in order to resolve the uncertainty. Likewise, Random Finite Set (RFS) is a method for single target and multi-target tracking. It provides a Bayesian recursion of multi-target distribution through the Finite Set calculus. Practical implementation of multi-target posterior recursion is too difficult. It was analytically proved that IPDA algorithm can be derived from the RFS based filter recursion under the linear Gaussian assumptions. Probability hypothesis density (PHD) filter is an alternative to this problem where only the first order moment of the complete multi-target posterior is propagated in time. In this article, IPDA and Gausian Mixtures PHD (GM PHD) filters in a single target tracking scenario are derived and compared. Simulations have demonstrated the superiority of IPDA filters in heavy clutters.

IPDA filters in the sense of Gaussian mixture PHD algorithm