Consensus Based Overlapping Decentralized Estimator

In this paper a new algorithm for state estimation is proposed in the form of a multi-agent network based on a synergy between local Kalman filters and a dynamic consensus strategy between the agents. It is shown that it is possible, under very general conditions concerning local resources and the network topology, to achieve asymptotic stability of the whole estimation algorithm by a proper choice of the consensus gains. It is demonstrated that the consensus gains can be obtained by minimizing the total mean-square estimation error. A number of examples illustrate efficiency and applicability of the proposed methodology.