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. >