期刊
AUTOMATICA
卷 114, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2020.108812
关键词
Extended Kalman filter; Lie groups; Differential Riccati equation
资金
- KUSTAR-KAIST Institute, KAIST
- KAIST, South Korea [G04170001, N11180231]
- Institute for Information & communications Technology Planning & Evaluation(IITP) - Korea government (MSIT) [2019-0-01396]
- ICT R&D program of MSIP/IITP, South Korea [2016-0-00563]
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. (C) 2020 Elsevier Ltd. All rights reserved.
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