パラメータ不確かさを有する非線形システムにおける連続・離散ロバスト拡張カルマンフィルタ

Abstract

This paper addresses state estimation problems for parametric uncertain nonlinear systems with linear measurements. A new Robust Extended Kalman Filter (REKF) which dose not involve augmented systems is devised. The REKF is more effective than the conventional EKF for uncertain systems. However, if the REKF is applied to nonlinear systems without parameter uncertainties, its accuracy will be lower than that of the conventional EKF. Then, we propose an Adaptive REKF(AREKF) to deal with this problem. Furthermore, we modify the predictive step of REKF in order to deal with continuous-discrete filtering problem. The validities of the proposed methods are illustrated in Monte Carlo simulations.