A consensus based overlapping decentralized estimator in lossy networks: Stability and denoising effects

In this paper a new algorithm for discrete-time overlapping decentralized state estimation has been analyzed. It is based on a combination of Kalman filters implemented by local agents using intermittent observations and a dynamic consensus strategy connecting the agents with possible communication faults. Under general conditions concerning the agent resources and the network topology, sufficient conditions for the asymptotic stability in the sense of bounded meansquare estimation error are derived. It is also demonstrated how the complexity of the multi-agent network contributes to the suppression of the measurement noise influence.