Copyright © 2009 Chunshi Fan and Zheng You. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nonlinearities in spacecraft attitude determination problem has
been studied intensively during the past decades. Traditionally,
multiplicative extended Kalman filter_MEKF_algorithm has been a
good solution for most nominal space missions. But in recent
years, advances in space missions deserve a revisit of the issue.
Though there exist a variety of advanced nonlinear filtering
algorithms, most of them are prohibited for actual onboard
implementation because of their overload computational complexity.
In this paper, we address this difficulty by developing a new
algorithm framework based on the marginal filtering principle,
which requires only 4 sigma points to give a complete 6-state
attitude and angular rate estimation. Moreover, a new strategy for
sigma point construction is also developed to further increase the
efficiency and numerical accuracy. Incorporating the presented
framework and novel sigma points, we proposed a new, nonlinear
attitude and rate estimator, namely, the Marginal Geometric Sigma
Point Filter. The new algorithm is of the same precision as
traditional unscented Kalman filters, while keeping a
significantly lower computational complexity, even when compared
to the reduced sigma point algorithms. In fact, it has truly
rivaled the efficiency of MEKF, even when simple closed-form
solutions are involved in the latter.