Stochastic moving horizon estimation for linear discrete-time systems with parameter variation

In this paper, we propose a novel state estimation algorithm for linear discrete time systems with modeling errors characterized by stochastic parameter variation. We extend the moving horizon estimation method to deal with a system with stochastic parameters and provide the optimal solution to a stochastic finite horizon state estimation problem. Furthermore, a numerical simulation shows the effectiveness of the proposed method.