Moving horizon estimation for networked systems with multiple packet dropouts

The moving horizon estimation (MHE) problem is investigated in this paper for a class of networked systems with multiple packet dropouts. The packet dropout process is modeled as a Bernoulli random process and the networked system is described as a stochastic parameter system model. By choosing a stochastic cost function, a novel solution strategy is presented for MHE optimization problem with multiple packet dropouts. By considering noise constraints, the LOQO algorithm is used to solve the constrained MHE problem. Moreover, the convergence properties of the estimator are studied. Finally, two examples are given to demonstrate the effectiveness of the proposed method and the practical advantages of the MHE method.

► A moving horizon estimation (MHE) algorithm is proposed for networked systems.
► A novel algorithm is proposed by introducing a stochastic cost function.
► The convergence properties of the moving horizon estimator are studied.
► The precision of the MHE is higher than that of the Kalman filtering.