A measurement-based robust adaptive kalman filtering algorithm

  • Yanhong Chang*
  • , Hai Zhang
  • , Qifan Zhou
  • *Corresponding author for this work

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

Abstract

In the case that the accuracy of standard kalman filter (SKF) declines when the noise statistical characteristics are unknown or changing, a measurement-based adaptive kalman filtering algorithm (MAKF) is presented. Based on the contrastive analysis of measurement characteristics of different measurement systems, MAKF is put forward to estimate adaptively the measurement noise variance R by co-difference measurement sequences. Simulation is performed by applying this algorithm to the GPS/INS integrated navigation system, the results show that MAKF can track the GPS measurement noise in real time on condition that the GPS measurement noise is unknown or changing, and the filtering accuracy and robustness are superior to those of SKF and an improved Sage-Husa adaptive kalman filtering algorithm.

Original languageEnglish
Title of host publicationMaterials Science and Information Technology, MSIT2011
Pages3773-3779
Number of pages7
DOIs
StatePublished - 2012
Event2011 International Conference on Material Science and Information Technology, MSIT2011 - Singapore, Singapore
Duration: 16 Sep 201118 Sep 2011

Publication series

NameAdvanced Materials Research
Volume433-440
ISSN (Print)1022-6680

Conference

Conference2011 International Conference on Material Science and Information Technology, MSIT2011
Country/TerritorySingapore
CitySingapore
Period16/09/1118/09/11

Keywords

  • Adaptive kalman filtering
  • GPS/INS
  • Integrated navigation
  • MAKF

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