Distributed Kalman filtering for time-varying discrete sequential systems

Abstract

Discrete sequential system (DSS) consisting of different dynamical subsystems is a sequentially-connected dynamical system, and has found applications in many fields such as automation processes and series systems. However, few results are focused on the state estimation of DSSs. In this paper, the distributed Kalman filtering problem is studied for time-varying DSSs with Gaussian white noises. A locally optimal distributed estimator is designed in the linear minimum variance sense, and a stability condition is derived such that the mean square error of the distributed estimator is bounded. An illustrative example is given to demonstrate the effectiveness of the proposed methods.