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Robust train localisation method based on advanced map matching measurement-augmented tightly-coupled GNSS/INS with error-state UKF

Published online by Cambridge University Press:  12 May 2023

Dan Liu Open the ORCID record for Dan Liu [Opens in a new window] ,
Wei Jiang ,
Baigen Cai ,
Oliver Heirich ,
Jian Wang  and
Wei Shangguan
Dan Liu
Affiliation:
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Wei Jiang*
Affiliation:
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Baigen Cai
Affiliation:
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Oliver Heirich
Affiliation:
Institute of Communications and Navigation, German Aerospace Center, Oberpfaffenhofen, Germany
Jian Wang
Affiliation:
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Wei Shangguan
Affiliation:
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
*
*Corresponding author: Wei Jiang; E-mail: [email protected]
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Abstract

This paper presents a robust train localisation system by fusing a Global Navigation Satellite System (GNSS) with an Inertial Navigation System (INS) in a tightly-coupled (TC) strategy. To improve navigation performance in GNSS partly blocked areas, an advanced map-matching (MM) measurement-augmented TC GNSS/INS method is proposed via an error-state unscented Kalman filter (UKF). The advanced MM generates a matched position using a one-step predicted position from a UKF time update step with binary search algorithm and a point–line projection algorithm. The matched position inputs as an additional measurement to fuse with the INS position to augment the degraded GNSS pseudorange measurement to optimise the state estimation in the UKF measurement update step. Both the real train test on the Qinghai–Tibet railway and the simulation were carried out and the results confirm that the proposed advanced MM measurement-augmented TC GNSS/INS with error-state UKF provides the best horizontal positioning accuracy of 0 ⋅ 67 m, which performs an improvement of about 71% and 90% with respect to TC GNSS/INS with only error-state UKF and only error-state Extended Kalman filter in GNSS partly blocked areas.

Keywords

Robust estimationGNSSinertial navigation system (INS)unscented Kalman filtermap
Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 2-3 , March 2023 , pp. 316 - 339
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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