Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter
Summary :
In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator 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 used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.8 pp.1565-1568
- Publication Date
- 2010/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E93.A.1565
- Type of Manuscript
- LETTER
- Category
- Systems and Control
Authors
Keyword
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Chin-Der WANN, Jian-Hau GAO, "Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 8, pp. 1565-1568, August 2010, doi: 10.1587/transfun.E93.A.1565.
Abstract: In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator 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 used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.1565/_p
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@ARTICLE{e93-a_8_1565,
author={Chin-Der WANN, Jian-Hau GAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter},
year={2010},
volume={E93-A},
number={8},
pages={1565-1568},
abstract={In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator 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 used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.},
keywords={},
doi={10.1587/transfun.E93.A.1565},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1565
EP - 1568
AU - Chin-Der WANN
AU - Jian-Hau GAO
PY - 2010
DO - 10.1587/transfun.E93.A.1565
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2010
AB - In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator 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 used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.
ER -
English
