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Lie group based nonlinear state errors for MEMS-IMU/GNSS/magnetometer integrated navigation

Published online by Cambridge University Press:  11 March 2021

Jiarui Cui Open the ORCID record for Jiarui Cui [Opens in a new window] ,
Maosong Wang ,
Wenqi Wu Open the ORCID record for Wenqi Wu [Opens in a new window]  and
Xiaofeng He
Jiarui Cui
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, P.R. China
Maosong Wang*
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, P.R. China
Wenqi Wu
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, P.R. China
Xiaofeng He
Affiliation:
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, P.R. China
*
*Corresponding author. E-mail: wangmaosong12@hotmail.com
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Abstract

In the integrated navigation system using extended Kalman filter (EKF), the state error conventionally uses linear approximation to tackle the commonly nonlinear problem. However, this error definition can diverge the filter in some adverse situations due to significant distortion of the linear approximation. By contrast, the nonlinear state error defined in the Lie group satisfies the autonomous equation, which thus has distinctively better convergence property. This work proposes a novel strapdown inertial navigation system (SINS) nonlinear state error defined in the Lie group and derives the SINS equations of the Lie group EKF (LG-EKF) for the MIMU/GNSS/magnetometer integrated navigation system. The corresponding measurement equations are also derived. A land vehicle field test has been conducted to evaluate the performance of EKF, ST-EKF (state transformation extended Kalman filter) and LG-EKF, which verifies LG-EKF's superior estimation accuracy of the heading angle as well as the other two horizontal angles (pitch and roll). The LG-EKF proposed in this paper is unlimited in the choice of sensors, which means it can be applied with both high-end and low-end inertial sensors.

Keywords

integrated navigationnonlinear state errorLG-EKFMIMU/GNSS/magnetometer integration
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 4 , July 2021 , pp. 887 - 900
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Copyright
Copyright © The Royal Institute of Navigation 2021

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