Receding horizon recursive state estimation

This paper describes a receding horizon discrete-time state observer using the deterministic least squares framework. The state estimation horizon, wh ich determines the number of past measurement samples used to reconstruct t he state vector, is introduced as a tuning parameter for the proposed state observer. A stability result concerning the choice of the state estimation horizon is established. It is also shown that the fixed memory receding ho rizon state observer can be related to the standard dynamic observer by usi ng an appropriate end point state weighting on the estimator cost function.