Sensor motion tracking by IMM-based extended Kalman filters

Chin Der Wann*, Jian Hau Gao

*Corresponding author for this work

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

2 Scopus citations

Abstract

In this paper, we present a real-time motion estimation and tracking scheme using interacting multiple model (IMM) based Kalman filters. In the proposed IMM-based structure, two filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized for sensor motion state estimation. In the QBEKF, measurements from gyroscope, accelerometer and magnetometer are processed; while in the GBEKF, sole measurements from gyroscope are processed. The interacting multiple model algorithm is capable of adaptively fusing the estimated states from the two models, and generating better estimation results via the flexibility of model weighting. Simulation results validate the proposed estimator design concept, and show that the scheme is capable of reducing the overall estimation errors.

Original languageEnglish
Title of host publication2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
Pages1377-1380
Number of pages4
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009 - Kaohsiung, Taiwan, Kaohsiung, Taiwan
Duration: 07 12 200909 12 2009

Publication series

Name2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

Conference

Conference2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
Country/TerritoryTaiwan
CityKaohsiung
Period07/12/0909/12/09

Keywords

  • Extended kalman filter
  • Interacting multiple model
  • Motion estimation
  • Motion tracking

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