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.

Meeting Name

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)


Document Type

Article - Conference proceedings

Document Version


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© 2018 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

01 Jan 2018