Optimal, Recursive and Suboptimal Linear Solutions to Attitude Determination from Vector Observations
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.
Origin : Files produced by the author(s)
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