Bearings-only localization using nonlinear second-order extended H∞ filter

Jwu-Sheng Hu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Simultaneous localization and mapping (SLAM) is an important issue in intelligent robotic research. The existing works perform robot localization using several nonlinear Bayesian filter such as extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter, etc. To cope with different types of disturbances other than Gaussian noise, this work proposes a nonlinear filter mechanism based on the H∞ control theory. First, a nonlinear system is introduced to be expanded by using Taylor's theorem. The 3rd and higher order term are ignored. The second order extended (SOE) Kalman filter is applied to show it performance comparing with the second order extended (SOE) H∞ filter. When the noise component is not perfect Gaussian distribution, which would usually happen in practical situation, the SOE H∞ filter outperform SOE Kalman filter. Also, the SOE H∞ filter requires less computation than particle filter which is more adequate to. A simulation result is shown and an experiment is design to test its real-time function.

Original languageEnglish
Title of host publication6th IFAC Symposium on Mechatronic Systems, MECH 2013
PublisherIFAC Secretariat
Number of pages6
ISBN (Print)9783902823311
StatePublished - 1 Jan 2013
Event6th IFAC Symposium on Mechatronic Systems, MECH 2013 - Hangzhou, China
Duration: 10 Apr 201312 Apr 2013

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670


Conference6th IFAC Symposium on Mechatronic Systems, MECH 2013


  • Bearing-only Localization
  • Nonlinear Estimation and Filtering
  • Robotics
  • Signal Processing

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