A moving horizon estimator performance bound

Abstract

Moving Horizon implementations of the Kalman Filter are widely used to overcome weaknesses of the Kalman Filter, or in problems when the Kalman Filter is not suitable. While these moving horizon approaches often perform well, it is of interest to encapsulate the loss in performance that comes when terms in the Kalman Filter are ignored. This paper introduces two methods to calculate a worst case performance bound on a Moving Horizon Kalman Filter.