Optimal linear estimation for networked systems with communication constraints

This paper is concerned with the optimal linear estimation problem for discrete time-varying networked systems with communication constraints. The communication constraint considered is that only one network node is allowed to gain access to a shared communication channel, then the various network nodes of the networked systems are scheduled to transmit data according to a specified media access control protocol, and a remote estimator performs the estimation task with only partially available measurements. The channel accessing processes of those network nodes are modeled by Bernoulli processes, and optimal linear filters are designed by using the orthogonal projection principle and the innovation analysis approach. It is shown that the optimal estimation performances critically depend on the channel accessing probabilities of the network nodes and the packet loss probability, and the optimal filters can be obtained by solving recursive Lyapunov and Riccati equations. An illustrative example is finally given to show the effectiveness of the proposed filters.