  The design of deterministic filters can be cast as a problem of minimizing an
associated cost function for an optimal control problem. Employing the min-plus
linearity property of the dynamic programming operator (associated with the
control problem) results in a computationally feasible approach (while avoiding
linearization of the system dynamics/output). This article describes the
salient features of this approach and a specific form of pruning/projection,
based on clustering, which serves to facilitate the numerical efficiency of
these methods.
