Fast Linear Quaternion Attitude Estimator Using Vector Observations

Abstract : As a key problem for multi-sensor attitude determination, Wahba’s problem has been studied for almost 50 years. Different from existing methods, this paper presents a novel linear approach to solving this problem. We name the proposed method the Fast Linear Attitude Estimator (FLAE) because it is faster than known representative algorithms. The original Wahba’s problem is extracted to several 1-dimensional equations based on quaternions. They are then investigated with pseudo-inverse matrices establishing a linear solution to ndimensional equations, which are equivalent to the conventional Wahba’s problem. To obtain the attitude quaternion in a robust manner, an eigenvalue-based solution is proposed. Symbolic solutions to the corresponding characteristic polynomial is derived showing higher computation speed. Simulations are designed and conducted using test cases evaluated by several classical methods e.g. M. D. Shuster’s QUaternion ESTimator (QUEST), F. L. Markley’s SVD method, D. Mortari’s Second Estimator of the Optimal Quaternion (ESOQ2) and some recent representative methods e.g. Y. Yang’s analytical method and Riemannian manifold method. The results show that FLAE generates attitude estimates as accurate as that of several existing methods but consumes much less computation time (about 50% of the known best algorithm). Also, to verify the feasibility in embedded application, an experiment on the accelerometer-magnetometer combination is carried out where the algorithms are compared via C++ programming language. An extreme case is finally studied, revealing a minor improvement shows more effectiveness in this case inspired by Y. Cheng et al.
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IEEE Transactions on Automation Science and Engineering, Institute of Electrical and Electronics Engineers, 2017
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Soumis le : vendredi 28 avril 2017 - 14:40:46
Dernière modification le : jeudi 15 juin 2017 - 09:09:37

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Jin Wu, Zebo Zhou, Bin Gao, Rui Li, Yuhua Cheng, et al.. Fast Linear Quaternion Attitude Estimator Using Vector Observations. IEEE Transactions on Automation Science and Engineering, Institute of Electrical and Electronics Engineers, 2017. <hal-01513263>

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