REKF and RUKF development for pico satellite attitude estimation in the presence of measurement faults

Halil Ersin Soken*, Chingiz Hajiyev

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

When a pico satellite is under normal operational conditions, whether it is Extended or Unscented, a conventional Kalman Filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms; Robust Extended Kalman Filter (REKF) and Robust Unscented Kalman Filter (REKF) for the case of measurement malfunctions. In both filters by the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristic of the accurate ones. Proposed robust Kalman filters are applied for the attitude estimation process of a pico satellite and the results are compared.

Original languageEnglish
Title of host publicationRAST 2011 - Proceedings of 5th International Conference on Recent Advances in Space Technologies
Pages891-896
Number of pages6
DOIs
Publication statusPublished - 2011
Event5th International Conference on Recent Advances in Space Technologies, RAST 2011 - Istanbul, Turkey
Duration: 9 Jun 201111 Jun 2011

Publication series

NameRAST 2011 - Proceedings of 5th International Conference on Recent Advances in Space Technologies

Conference

Conference5th International Conference on Recent Advances in Space Technologies, RAST 2011
Country/TerritoryTurkey
CityIstanbul
Period9/06/1111/06/11

Keywords

  • attitude estimation
  • EKF
  • robust Kalman filtering
  • UKF

Fingerprint

Dive into the research topics of 'REKF and RUKF development for pico satellite attitude estimation in the presence of measurement faults'. Together they form a unique fingerprint.

Cite this