A generalization of the PDA target tracking algorithm using hypothesis clustering

A suboptimal algorithm is proposed for target tracking in clutter. The exact posterior density of a target state conditioned on the past observation history is a Gaussian mixture with the number of terms equal to the number of possible ways to associate observations and targets. In order to avoid an exponentially growing complexity, the algorithm performs an approximation by naturally partitioning and grouping the target state estimates into a set of approximate sufficient statistics. A new criterion function is introduced in this approximation process. The well-known probabilistic data association (PDA) filter is a special case of the algorithm.