Convergence results in distributed Kalman filtering

Abstract—The paper studies the convergence properties of the estimation error processes in distributed Kalman filtering for potentially unstable linear dynamical systems. In particular, it is shown that, in a weakly connected communication network, there exist (randomized) gossip based information dissemination schemes leading to a stochastically bounded estimation error at each sensor for any non-zero rate γ of intersensor communication (the rate γ is defined to be the average number of inter-sensor communications per signal evolution epoch). A gossipbased information exchange protocol, the M-GIKF, is presented, in which sensors exchange estimates and aggregate observations at a rate γ > 0, leading to desired convergence properties. Under the assumption of global (centralized) detectability of the signal/observation model (necessary for a centralized estimator having access to all sensor observations at all times to yield bounded estimation error), it is shown that the distr...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Soummya Kar, Shuguang Cui, H. Vincent Poor, José M. F. Moura
• Keywords: Convergence Properties, Estimation Error, ICASSP 2011, Sensor, Signal Processing