Research on Train Positioning Method Based on Maximum Correntropy Robust Filtering with Dynamic Kernel Bandwidth

Accurate and reliable train positioning is essential for railway operation control systems. However, conventional extended Kalman filter-based solutions are vulnerable to measurement faults, which can significantly degrade positioning performance. To address this issue, this paper proposes an adaptive maximum correntropy robust filter (AMCRF) for a GNSS/INS-based train positioning system. The loss function of the extended Kalman filter is reformulated from the minimum mean square error criterion to a maximumcorrentropy criterion, thereby improving the algorithm’s robustness against measurement faults. In AMCRF, considering the limitation of using a fixed kernel bandwidth, a lion swarm optimization strategy is introduced to adaptively tune the kernel bandwidth for each visible satellite, enabling the filter to adapt to time-varying measurement quality and fault magnitudes. By embedding the adaptive mechanism into an extended Kalman filtering framework, the proposed method achieves enhanced fault tolerance. The effectiveness of the proposed AMCRF is validated using experimental data collected along the Qinghai–Tibet Railway. Step and ramp faults of different magnitudes are injected into pseudorange measurements to evaluate fault tolerance. Experimental results demonstrate that the proposed method effectively suppresses the influence of faulty measurements and maintains positioning accuracy close to that under fault-free conditions.