Constrained state estimation for nonlinear systems with non-Gaussian noise
Shinji Ishihara, Masaki Yamakita · 2009
This paper addresses a state-estimation problem for nonlinear systems with non-Gaussian noise and interval constraints on the state vector. We propose new efficient algorithms, which are based on unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF). We use truncated UKF (TUKF) in Gaussian sum filter (GSF) framework, which is named constrained unscented GSF (CUGSF). And we proposed an efficient constrained EnKF (E-CEnKF), which does not require to solve complicate optimization problem like the conventional method. Validity of the proposed methods are illustrated in numerical examples.