Novel quaternion Kalman filter

This paper presents a novel Kalman filter (KF) for estimating the attitude-quaternion as well as gyro random drifts from vector measurements. Employing a special manipulation on the measurement equation results in a linear pseudo-measurement equation whose error is state-dependent. Because the quate...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2006-01, Vol.42 (1), p.174-190
Main Authors: Choukroun, D., Bar-Itzhack, I.Y., Oshman, Y.
Format: Article
Language:English
Subjects:
Aerodynamics
Computer simulation
Covariance matrix
Dynamical systems
Dynamics
Equations
Errors
Estimation error
Filters
Mathematical analysis
Mathematical models
Matrices
Matrix methods
Parameter estimation
Quaternions
Space vehicles
Studies
Vectors
Yield estimation
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Summary:This paper presents a novel Kalman filter (KF) for estimating the attitude-quaternion as well as gyro random drifts from vector measurements. Employing a special manipulation on the measurement equation results in a linear pseudo-measurement equation whose error is state-dependent. Because the quaternion kinematics equation is linear, the combination of the two yields a linear KF that eliminates the usual linearization procedure and is less sensitive to initial estimation errors. General accurate expressions for the covariance matrices of the system state-dependent noises are developed. In addition, an analysis shows how to compute these covariance matrices efficiently. An adaptive version of the filter is also developed to handle modeling errors of the dynamic system noise statistics. Monte-Carlo simulations are carried out that demonstrate the efficiency of both versions of the filter. In the particular case of high initial estimation errors, a typical extended Kalman filter (EKF) fails to converge whereas the proposed filter succeeds.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2006.1603413