A Comparison of Linear Attitude Estimators
This work compares the performance of linear attitude estimators; in particular, the classic multiplicative extened Kalman filter and unscented Kalman filter performances are compared to a recently introduced novel spacecraft attitude estimation algorithm. The new algorithm utilizes unit vector measurements and is also based on the unscented Kalman filter (UKF). The UKF, like the extended Kalman filter, is an approximation of the linear minimum mean square error estimator and employs a linear update with an additive residual. The standard formulation of the residual is given by the difference between the measurement and its mean. The recently proposed algorithm, on the other hand, utilizes a multiplicative residual, which is more consistent with the nature of unit direction measurements. The recent algorithm consistently defines attitude errors utilizing the Gibbs vector parameterization and computes averages and deviations consistently with attitude composition rules.
R. Zanetti and K. J. Demars, "A Comparison of Linear Attitude Estimators," Proceedings of the Space Flight Mechanics Meeting (2018, Kissimmee, FL), no. 210009, American Institute of Aeronautics and Astronautics (AIAA), Jan 2018.
The definitive version is available at https://doi.org/10.2514/6.2018-0470
Space Flight Mechanics Meeting, 2018 (2018: Jan. 8-12, Kissimmee, FL)
Mechanical and Aerospace Engineering
Keywords and Phrases
Bearings (machine parts); Mean square error; Mechanics; Signal receivers; Space flight, Attitude error; Composition rule; Direction measurement; Gibbs vector; Linear minimum mean square errors; Spacecraft attitude estimation; Unit vectors; Unscented Kalman Filter, Kalman filters
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2018 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.
01 Jan 2018