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 |
|---|---|
| Title of host publication | SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems |
| Pages | 1261-1264 |
| Number of pages | 4 |
| Publication status | Published - 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 |
|---|---|
| Country | Japan |
| City | Okayama |
| Period | 12/8/10 → 12/12/10 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
Cite this
An Unscented rauch-tung-striebel smoother for a vehicle localization problem. / Razali, Saifudin; Watanabe, Keigo; Maeyama, Shoichi; Izumi, Kiyotaka.
SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. 2010. p. 1261-1264.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An Unscented rauch-tung-striebel smoother for a vehicle localization problem
AU - Razali, Saifudin
AU - Watanabe, Keigo
AU - Maeyama, Shoichi
AU - Izumi, Kiyotaka
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84866656625&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84866656625
SP - 1261
EP - 1264
BT - SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems
ER -