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

Saifudin Razali, Keigo Watanabe, Shoichi Maeyama, Kiyotaka Izumi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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
Issue number7
Publication statusPublished - Sept 2011


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

ASJC Scopus subject areas

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


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