Partitioning: A unifying framework for adaptive systems, I: Estimation

Abstract

In this paper, partitioning and the associated generalized partitioned estimation algorithms are shown to constitute a unifying and powerful framework for optimal adaptive estimation in linear as well as nonlinear problems. Using the partitioning framework, the adaptive estimation problem is treated from a global viewpoint that readily yields and unifies seemingly unrelated results and, most importantly, yields fundamentally new families of nonlinear and linear estimation algorithms in a decoupled parallel-realization form. The generalized partitioned estimation algorithms are shown to have several important properties from both a theoretical and a realization or computational standpoint.