Distributed filtering with randomly occurring uncertainties over sensor networks: the channel fading case

This paper is concerned with the distributed filtering problem for a class of uncertain stochastic systems with fading channels over sensor networks. The norm-bounded uncertainty enters into the system in a random way, and the Bernoulli distributed white sequence is introduced to govern the random occurrences of such uncertainties. The lossy sensor network suffers from the phenomenon of fading measurements that are described by a modified lth-order Rice fading model in which the channel coefficients can have any probability density function on the interval [0, 1]. Through available output measurements from not only the individual sensor but also its neighboring sensors, a sufficient condition is established for the desired distributed filter to ensure that the filtering dynamics is exponentially mean-square stable and the prescribed performance constraint is satisfied. The distributed filter gains are characterized by solving an auxiliary convex optimization problem. Finally, a simulation example is provided to show the effectiveness of the proposed filtering scheme.