Tightly-coupled GNSS/INS/Magnetic fusion for seamless train localization in GNSS challenged environments

Abstract

Accurate and continuous train localization is critical for modern intelligent railway systems. However, it remains a significant challenge to achieve seamless navigation in global navigation satellite systems (GNSS) challenged environments, such as long tunnels, deep valleys, and canopies. During prolonged GNSS outages, conventional systems struggle to constrain unbounded inertial drift, while traditional magnetic matching methods suffer from spatial ambiguity and dynamic distortions in time-domain sequence. To address these limitations, this paper proposes a tightly-coupled GNSS/inertial navigation system (INS)/Magnetic fusion method based on a kinematics-assisted spatial sequence matching algorithm. By leveraging high-frequency INS velocity estimates, temporal magnetic data are resampled into the spatial domain. A localized scale compensation strategy is proposed to correct INS integration errors, and the resulting high-confidence spatial updates are seamlessly incorporated into an extended Kalman filter. Extensive field tests conducted on a diesel locomotive over a 6 km railway trajectory demonstrate that the proposed method effectively suppresses trajectory oscillations and achieves a positioning root mean square error of 2.03 m. Furthermore, the system exhibits robust performance even with heavily sparsified magnetic maps at a sampling interval of up to 10 m, substantially reducing map storage overhead and highlighting its strong potential for large-scale engineering deployment.