A General Iterative Extended Kalman Filter Framework for State Estimation on Matrix Lie Groups

Ben Liu; Hua Chen; Wei Zhang

10.1109/CDC49753.2023.10383770

2023-12

2023 62nd IEEE Conference on Decision and Control (CDC)

0743-1546

2576-2370

979-8-3503-0125-0

2023 62nd IEEE Conference on Decision and Control (CDC)

1177-1182

December 13-15, 2023

Marina Bay Sands, Singapore

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In this paper, we focus on state estimation problem for nonlinear systems on joint matrix Lie group G and Euclidean space Rn. We propose a general iterative Kalman filter, aiming to integrate the prediction step into the iteration scheme, which is not considered in the conventional iterative extended Kalman filter framework. Such an extra iteration scheme in the prediction step helps improving the accuracy of probability density function propagation through nonlinearities, which can further lead to more accurate estimations of the system states. In addition, the proposed framework unifies the Kalman filter based estimation schemes on studied manifold by adopting the perspective from Gaussian Bayesian inference. The improvement of the proposed framework is illustrated by the ES-GIKF algorithm that is instantiated from the proposed framework in a numerical simulation.

Manifolds Probability density function Numerical simulation Inference algorithms Numerical models Bayes methods Kalman filters

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China["62073159","62003155"] ; Shenzhen Science and Technology Program[JCYJ20200109141601708] ; Shenzhen Key Laboratory of Control Theory and Intelligent Systems[ZDSYS20220330161800001]

Automation & Control Systems ; Engineering

Automation & Control Systems ; Engineering, Electrical & Electronic

WOS:001166433800143

20240715548215

人工提交

Southern University of Science and Technology

Ben Liu,Hua Chen,Wei Zhang. A General Iterative Extended Kalman Filter Framework for State Estimation on Matrix Lie Groups[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1177-1182.