Track-Constrained Dual-Baseline Fusion Algorithm for Parallel Train Integrity Monitoring and Positioning with Reduced Sensitivity on Track Curvature

Conventional methods for train positioning and integrity monitoring are limited by their dependence on trackside infrastructure. This reliance on fixed equipment has prompted the investigation of global navigation satellite systems (GNSSs) as a more efficient alternative. The track-constrained algorithm based on the ‘train head (TH) and train tail (TT)’ double-difference (DD) baseline model (Single DD algorithm) has been applied for positioning and train length monitoring. It has been observed that the coefficient matrix can cause the inflation of the odometer corrections when the difference in track slope at both ends of the train is small. This inflation problem reduces the train positioning accuracy. A dual DD baseline fusion algorithm (Dual DD algorithm) with minimized sensitivity on the difference in track slope is thereby introduced. Furthermore, to validate the status of reference stations, a cross-checking function is utilized. The simulation results demonstrate that with a noise setting of 0.0067 m in carrier phase measurement, the Dual DD algorithm enhances the accuracy of train location estimation by up to 10 times compared to the Single DD algorithm. Meanwhile, the simulation result of train length difference validates the feasibility of the cross-checking function.