Distributed Kalman consensus filter with intermittent observations

摘要

We consider the problem of distributed state estimation for linear time-varying systems with intermittent observations. An optimal Kalman consensus filter has been developed by minimizing the mean-squared estimation error for each node. To derive a scalable algorithm for the covariance matrices update, a suboptimal filter is proposed by omitting the edge covariance matrices among nodes. By using the Lyapunov-based approach, a sufficient condition is presented for ensuring the stochastic stability of the suboptimal filter. Two numerical examples are provided to verify the effectiveness of the proposed filter. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.