Plenary Talk: State Estimation Using Distributed Processing

摘要

The problem of estimating the state of a linear system whose measured outputs are distributed across a network has been under study in one form or another for a number of years. Despite this, only recently have provably correct distributed state observers emerged which solve this problem under reasonably non - restrictive assumptions. The aim of this talk is to describe some of these observers and the conditions under which they can provide asymptotically correct state estimates. For any of these observers to function robustly in the face of small modeling errors, it is necessary for the process whose state is to be estimated to be stable. Interestingly this stability requirement is also necessary for centralized {robust} state estimation, whether the estimator is a classical observer or even a Kalman filter.