An Unscented Rauch-Tung-Striebel Smoother for a Vehicle Localization Problem
Saifudin Razali, Keigo Watanabe, Shoichi Maeyama, Kiyotaka Izumi · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2011
The Unscented Kalman Filter (UKF) has become relatively a new technique used in a number of nonlinear estimation problems to overcome the limitation of Taylor series linearization. It uses a deterministic sampling approach known as sigma points to propagate nonlinear systems and has been discussed in many literature. However, a nonlinear smoothing problem has received less attention than the filtering problem. Therefore, in this article an unscented smoother based on Rauch-Tung-Striebel formis examined for discretetime dynamic systems. It has advantages available in unscented transformation over approximation by Taylor expansion as well as its benefit in derivative free. To show the effectiveness of the proposed method, the unscented smoother is implemented and evaluated through a vehicle localization problem.