An unscented Rauch-Tung-Striebel smoother for a vehicle localization problem

Saifudin Razali, Keigo Watanabe, Shoichi Maeyama, Kiyotaka Izumi

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 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.

Original languageEnglish
Pages (from-to)860-868
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume15
Issue number7
Publication statusPublished - Sep 2011

Fingerprint

Taylor series
Linearization
Kalman filters
Nonlinear systems
Dynamical systems
Sampling
Derivatives

Keywords

  • Rauch-Tung-Striebel smoother
  • Unscented transformation
  • Vehicle localization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

An unscented Rauch-Tung-Striebel smoother for a vehicle localization problem. / Razali, Saifudin; Watanabe, Keigo; Maeyama, Shoichi; Izumi, Kiyotaka.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 7, 09.2011, p. 860-868.

Research output: Contribution to journalArticle

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