LAUSR

This technical note studies the state estimation problem for stochastic complex networks with switching topology. A set of Bernoulli random variables are used to describe the switching behavior of the network. By using the structure of the extended Kalman filter (EKF), a recursive estimator is developed for each node to guarantee an optimized upper bound on the state estimation error covariance despite the switching coupling and linearization errors. Compared with the existing results on state estimation for stochastic complex networks, a distinct feature for the proposed estimator is that the estimation errors for all nodes are not formulated in an augmented vector so that the gain matrix can be determined separately for each node by solving two Riccati-like difference equations. Based on the stochastic analysis theory, it is shown that the estimation error is bounded in mean square under certain conditions. Simulation results are provided to verify the effectiveness of the proposed estimator.