Distributed Kalman consensus filter with intermittent observations

Wenling Li, Yingmin Jia, Junping Du

Abstract: 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.