Invariant extended Kalman filter on matrix Lie groups

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We derive symmetry preserving invariant extended Kalman filters (IEKF) on matrix Lie groups. These Kalman filters have an advantage over conventional extended Kalman filters as the error dynamics for such filters are independent of the group configuration which, in turn, provides a uniform estimate of the region of convergence. The proposed IEKF differs from existing techniques in literature on the account that it is derived using minimal tools from differential geometry that simplifies its representation and derivation to a large extent. The filter error dynamics is defined on the Lie algebra directly instead of identifying the Lie algebra with an Euclidean space or defining the error dynamics in local coordinates using exponential map, and the associated differential Riccati equations are described on the corresponding space of linear operators using tensor algebra. The proposed filter is implemented for the attitude dynamics of the rigid body, which is a benchmark problem in control, and its performance is compared against a conventional extended Kalman filter (EKF). Numerical experiments support that the IEKF is computationally less intensive and gives better performance than the EKF.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2020-04
Language
English
Article Type
Article
Citation

AUTOMATICA, v.114

ISSN
0005-1098
DOI
10.1016/j.automatica.2020.108812
URI
http://hdl.handle.net/10203/273783
Appears in Collection
EE-Journal Papers(저널논문)
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