Distributed Robust Optimal Filter Design Over Sensor Networks With Data Packet Dropouts

This paper studies the problem of distributed state estimation for discrete-time systems over a sensor network with unreliable transmission. Each sensor node constructs a local estimate based on its own observation and on those collected from its neighbors through lossy links. A nonzero sum Nash game is used to deal with such a multiobjective distributed filtering problem. Stabilization solutions in the mean square sense are established for a set of cross-coupled modified algebraic Riccati equations associated with each sensor node. Based on mean square stabilization solutions, causal Nash equilibrium strategies, consisting of the local optimal filter gains and the worst case disturbance signals, are further analytically conducted. Finally, a numerical example is included to show the validity of the current results.