A filtering method of gyroscope random drift for miniature unmanned helicopter

Yue Pan, Ping Song*, Kejie Li, Ran Lin, Wei Huang

*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

Low-cost MEMS gyroscopes used in the Miniature Unmanned Helicopter (MUH) have great random drift. In order to improve the performance of MEMS gyroscopes, a one-order AR model was established for the random drift. Then Sage-Husa adaptive Kalman filter was applied to process the random drift signal. Experiments were carried out to verify the validity of the method. Tests results demonstrate that the method based on AR model and Sage-Husa adaptive Kalman filter is convenient and effective and it significantly reduces random drift. Compared to conventional Kalman filter, Sage-Husa adaptive Kalman filter can estimate statistic characteristics of system noise and measurement noise and modify filter parameters on-time. It improves stability and adaptability and thus can give a more accurate filtering result.

Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Pages730-734
Number of pages5
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 - Harbin, China
Duration: 24 Dec 201126 Dec 2011

Publication series

NameProceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Volume2

Conference

Conference2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Country/TerritoryChina
CityHarbin
Period24/12/1126/12/11

Keywords

  • AR model
  • MUH
  • Sage-Husa adaptive Kalman filter
  • gyroscope random drift

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