A satellite observation data considered train positioning optimization method with RTK

Abstract

In this paper, a novel train positioning method considering satellite raw observation data was proposed, which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources. The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results. Specifically, the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality, and then, GNSS combined observation models were adopted to construct the detection values, which were applied to judge abnormal-data through the dual-frequency observations. Further, ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments. The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability. Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data. After adopting the data selection strategy, the ambiguity fixing percentage was improved by 29.2%, and the standard deviation in the East, North, and Up components was enhanced by 12.7%, 7.4%, and 12.5%, respectively. The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.

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