4.7 Article

Invariant extended Kalman filter on matrix Lie groups

Journal

AUTOMATICA
Volume 114, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2020.108812

Keywords

Extended Kalman filter; Lie groups; Differential Riccati equation

Funding

  1. KUSTAR-KAIST Institute, KAIST
  2. KAIST, South Korea [G04170001, N11180231]
  3. Institute for Information & communications Technology Planning & Evaluation(IITP) - Korea government (MSIT) [2019-0-01396]
  4. ICT R&D program of MSIP/IITP, South Korea [2016-0-00563]

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available