The Railway BeiDou ground-based augmentation network is a critical infrastructure for railway precision engineering, providing continuous high-precision time-space reference and positioning services to the surrounding areas of the railway tracks.The railway BeiDou ground-based augmentation network is often characterized by a strip-like distribution, which exhibits significant spatial distribution differences when compared to regional ground-based augmentation networks with a planar distribution.In order to achieve optimal positioning service performance in a strip-like network scenario, this paper analyzes the error propagation patterns of three spatially correlated error correction models, namely distance interference model (DIM), linear interpolation model(LIM), and least squares configuration model(LSC), within the context of railway strip scenarios.Additionally, it combines practical experiments on the railway BeiDou ground-based augmentation network to conduct high-precision positioning studies.The results indicate that within the railway ground-based augmentation Network, the LSC model demonstrates excellent positioning service performance both at the network's edges and in peripheral locations, with real-time positioning accuracy better than 2 cm for both horizontal and vertical dimensions, and minimal fluctuations in accuracy with changes in service locations.The LIM performs well only at the network edges and along its alignment, achieving an accuracy of up to 2 cm, but experiences a significant decrease in positioning accuracy when far from the ground-based augmentation network.The DIM exhibits poor accuracy and reliability in positioning.Unlike regional ground-based augmentation networks, in the railway strip network scenario, the performance of the LSC in positioning services surpasses that of the LIM and DIM.
维普
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