Research on Railway Train Space Position Correction Algorithm Based on GNSS Correction Model

The traditional railway train positioning methods mainly include three methods: speed sensor based, ground point responder response, and track circuit section occupancy detection. However, the traditional positioning method does not respond in a timely manner and is gradually being replaced by new positioning methods. With the development of high-speed rail in China, the Global Navigation Satellite System (GNSS) is gradually being implemented as a new positioning system for railway trains, and auxiliary technologies such as train accelerometers and odometry are integrated to ensure the safe operation of trains. However, GNSS can meet the positioning accuracy requirements in strong signal areas, but the positioning accuracy of trains is lower in weak signal or no satellite signal environments, resulting in certain positioning errors or noise, which cannot meet the needs of analysis and decision-making in geographic information systems. This paper uses the Unscented Kalman Filter algorithm to improve and design a GNSS based positioning correction model. Based on railway network GIS data, an electronic map spatial position correction matching method is proposed to improve and solve the problem of train positioning accuracy errors in weak signal environments. After experimental research, the GNSS positioning correction model and electronic map matching method designed in this paper can improve the fusion expression accuracy of train positioning on electronic maps, achieving the predetermined goal. © 2024 Codon Publications. All rights reserved.