Abstract
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 we examine an unscented smoother based on Rauch-Tung-Striebel form for discrete-time dynamic systems. This smoother has advantages available in unscented transformation over approximation by Taylor expansion as well as its benefit in derivative free. This smoothing technique has been implemented and evaluated through vehicle localization problem.
Original language | English |
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Pages | 1261-1264 |
Number of pages | 4 |
Publication status | Published - Dec 1 2010 |
Event | Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan Duration: Dec 8 2010 → Dec 12 2010 |
Other
Other | Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 |
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Country/Territory | Japan |
City | Okayama |
Period | 12/8/10 → 12/12/10 |
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems