Distributed state estimation for stochastic discrete-time sensor networks with redundant channels

Abstract

This paper investigates the distributed state estimation problem for a class of sensor networks described by discrete-time stochastic systems with stochastic switching topologies. In the sensor network, the redundant channel and the randomly varying nonlinearities are considered. The stochastic Brownian motions affect both the dynamical plant and the sensor measurement outputs. Through available output measurements from each individual sensor, the distributed state estimators are designed to approximate the states of the networked dynamic system. Sufficient conditions are established to guarantee the convergence of the estimation error systems for all admissible stochastic disturbances and randomly varying nonlinearities. Then, the distributed state estimator gain matrices are derived using the linear matrix inequality method. Moreover, a numerical example is given to verify the effectiveness of the theoretical results.