Distributed estimators for nonlinear systems

A nonlinear distributed estimation problem is solved by using reduced-order local models. Using local models with lower dimensions than the observed process model will lessen the local processors´ complexities or computational loads. Fusion algorithms that combine local densities to construct the centralized density of a nonlinear random process are presented. The local densities are generated at each measurement time and communicated to a coordinator. The models used to produce these densities are reduced-order valid models. The validity of the local models guarantees that the coordinator reconstructs exactly the centralized density function