International audienceThis paper proposes a novel quaternion-basedattitude estimator with magnetic, angular rate, and gravity (MARG) sensor arrays. A new structure of a fixed-gaincomplementary filter is designed fusing related sensors. To avoidusing iterative algorithms, the accelerometer-based attitude determination is transformed into a linear system. Stable solutionto this system is obtained via control theory. With only onematrix multiplication, the solution can be computed. Using theincrement of the solution, we design a complementary filter thatfuses gyroscope and accelerometer together. The proposed filteris fast, since it is free of iteration. We name the proposed filter thefast complementary filter (FCF). To decrease significant effectsof unknown magnetic distortion imposing on the magnetometer, a stepwise filtering architecture is designed. The magneticoutput is fused with the estimated gravity from gyroscope andaccelerometer using a second complementary filter when thereis no significant magnetic distortion. Several experiments arecarried out on real hardware to show the performance andsome comparisons. Results show that the proposed FCF canreach the accuracy of Kalman filter. It successfully finds abalance between estimation accuracy and time consumption.Compared with iterative methods, the proposed FCF has muchless convergence speed. Besides, it is shown that the magneticdistortion would not affect the estimated Euler angles
International audienceAs a key problem for multi-sensor attitudedetermination, Wahba’s problem has been studied for almost50 years. Different from existing methods, this paper presentsa novel linear approach to solving this problem. We name theproposed method the Fast Linear Attitude Estimator (FLAE)because it is faster than known representative algorithms. Theoriginal Wahba’s problem is extracted to several 1-dimensionalequations based on quaternions. They are then investigatedwith pseudo-inverse matrices establishing a linear solution to ndimensional equations, which are equivalent to the conventionalWahba’s problem. To obtain the attitude quaternion in a robustmanner, an eigenvalue-based solution is proposed. Symbolicsolutions to the corresponding characteristic polynomial isderived showing higher computation speed. Simulations aredesigned and conducted using test cases evaluated by severalclassical methods e.g. M. D. Shuster’s QUaternion ESTimator(QUEST), F. L. Markley’s SVD method, D. Mortari’s SecondEstimator of the Optimal Quaternion (ESOQ2) and some recentrepresentative methods e.g. Y. Yang’s analytical method andRiemannian manifold method. The results show that FLAEgenerates attitude estimates as accurate as that of severalexisting methods but consumes much less computation time(about 50% of the known best algorithm). Also, to verifythe feasibility in embedded application, an experiment onthe accelerometer-magnetometer combination is carried outwhere the algorithms are compared via C++ programminglanguage. An extreme case is finally studied, revealing a minorimprovement shows more effectiveness in this case inspired byY. Cheng et al
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