Nonlinear receding-horizon state estimation by real-time optimization technique

A real-time optimization technique is proposed for optimal state estimation of general nonlinear systems. A state estimation algorithm is determined so that a receding-horizon performance index is minimized. Application of the stabilized continuation method results in a real-time solution technique that does not involve any approximation or iterative algorithms in principle. The discretization of the algorithm is the only approximation required in the actual implementation on digital computers. The structure of the state estimation algorithm is clarified based on the present solution technique, and a simple model of a nonlinear space vehicle is employed as an application example. Results of simulation and experiment validate the effectiveness of the proposed algorithm.