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Improvement of Attitude Estimation Using Hidden Markov Model Classification

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Authors
Kang, Chul Woo; Park, Chan Gook
Issue Date
2010-04
Citation
AIAA Infotech@Aerospace 2010 Conference and Exhibit
Abstract
This paper introduces the attitude estimation method of AHRS using an Extended
Kalman Filter(EKF) with a filter tuning algorithm based on Hidden Markov Model(HMM).
The AHRS uses inertial sensors and magnetometers to calculate its attitude. It is known that
the attitude update using gyros are prone to diverge and hence the attitude error needs to
compensate using accelerometers and magnetometers. In this paper, a Kalman filter model
with a state variables represented by a quaternion is presented and a model changing
algorithm is used to make the filter more robust to acceleration and magnetic disturbances.
If the AHRS measures any disturbances which are caused by movement of the vehicle,
HMM estimates the existence of disturbances. Using these estimates HMM changes the filter
gain using tuning parameters of the filter. Results of EKF tuned by HMM indicate that the
proposed method makes robust to disturbances more properly.
Language
English
URI
http://hdl.handle.net/10371/72165
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Others_기계항공공학부
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