Simple tracking algorithms based upon nearest neighbor filtering do not correctly consider measurement origin uncertainty and, therefore, fail to perform well in situations of high target density and clutter. The optimal tracking algorithm for commonly used targetclutter models computes the posterior density of the target state conditioned on the past history of' observations. This posterior density is a Gaussian mixture with the number of terms equal to the number of possible ways to associate observations and targets. Though a recursive algorithm may be developed for the optimal estimator, it requires exponentially growing rlemory and computation and is, therefore, unimplementable. In this paper a new suboptimal algorithm is proposed where approximation is done by naturally partitioning and grouping the target state estimates into a set of approximate sufficient statistics. A new critel-ion fu~lctioi~s li iltroduced in this approximation process. The well-known Probabilistic Data Association filter (PDAF) turns out to be a special case of the new algorithm. Comparisons are made for the proposed estimator versus the PDAF.
Date of this Version