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Optimal, Recursive and Suboptimal Linear Solutions to Attitude Determination from Vector Observations

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Abstract

This paper deals with the optimal attitude determination problem and its sub-optimal and time-varying recursive variants. The developed methods are named as the Optimal Linear Estimator of Quaternion (OLEQ), Suboptimal-OLEQ (SOLEQ) and Recursive-OLEQ (ROLEQ). The theory is established based on our previous contributions and the multivector matrix multiplications are decomposed with the eigenvalue factorization. Some analytical results are proved and given which provides the audience with a brand new viewpoint of the attitude determination and its evolution inside. With the derivations of two-vector case, the n-vector cased is then naturally formed. Simulations are carried out showing the advantages of accuracy, robustness and time consumption of the proposed OLEQs, compared with representative methods. The algorithms are then implemented using C++ programming language on the designed hardware with 3-axis accelerometer, 3-axis magnetometer, giving the effectiveness validation of them in real-world applications.
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Dates and versions

hal-01525603 , version 1 (22-05-2017)
hal-01525603 , version 2 (20-07-2017)
hal-01525603 , version 3 (09-08-2017)
hal-01525603 , version 4 (11-12-2017)

Licence

Attribution - CC BY 4.0

Identifiers

  • HAL Id : hal-01525603 , version 4

Cite

Zebo Zhou, Jin Wu, Jinling Wang, Hassen Fourati. Optimal, Recursive and Suboptimal Linear Solutions to Attitude Determination from Vector Observations. 2017. ⟨hal-01525603v4⟩
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