Estimation for Nonlinear Dynamical Systems over Packet-Dropping Networks

Two approaches, extended Kalman filter (EKF) and moving horizon estimation (MHE), are discussed for state estimation for nonlinear dynamical systems over packet-dropping networks. For EKF, we provide sufficient conditions that guarantee a bounded EKF error covariance. For MHE, a natural scheme on organizing the finite horizon window is proposed to handle intermittent observations. A nonlinear programming software package, SNOPT, is employed in MHE and the formulation for constraints is discussed in detail. Examples and simulation results are presented.