Invariant extended Kalman filter on matrix Lie groups

被引:22
|
作者
Phogat, Karmvir Singh [1 ]
Chang, Dong Eui [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 34141, South Korea
关键词
Extended Kalman filter; Lie groups; Differential Riccati equation;
D O I
10.1016/j.automatica.2020.108812
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:6
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