High-Speed Train Positioning Using Improved Extended Kalman Filter With 5G NR Signals
Tao Wen, Hao Jiang, Baigen Cai, Clive Roberts
Abstract
With the integration of 5G NR (New Radio) into railway systems, the demand for enhanced positioning and trajectory tracking performance in High-Speed Train (HST) networks has grown. However, many existing train positioning schemes rely on traditional algorithms like the Extended Kalman Filter (EKF), which may fall short of meeting the precision requirements, particularly in 5G HST scenarios. Addressing this limitation, this paper draws on the concepts presented by Ko et al. (2022) and introduces an Improved Extended Kalman Filter (IEKF) using the Least Squares of Undermeasurement (LSU) technique, specifically tailored for nonlinear systems. The IEKF, expanding step by step, theoretically captures statistical properties of the Knorr set for any order prediction error, providing richer information on higher-order terms compared to the traditional EKF. Additionally, for a more intuitive comparison of the IEKF unfolded to different orders, a novel performance indicator is introduced. In conclusion, to validate the effectiveness of our proposed algorithm in real-world scenarios, we demonstrate its superior localization performance by comparing Mean Squared Error (MSE) and Mean Absolute Error (MAE) with traditional nonlinear localization algorithms. The comparisons are based on simulation examples involving train localization tracking and an industrial device ablation system.