A Positioning and Navigation System for Railway Technical Station Operators
As an important node in the dismantling and grouping of freight trains, the operational tasks of the railway technical station are increasingly busy. Various business activities involve a large number of field personnel, and ensuring the personal safety and operational efficiency of these personnel is one of these issues that railway operations need to address.To ensure the operational safety and standardization at the railway technical station, high-precision positioning system for the personnel is required. However, typical operational scenarios at railway technical stations often involve problems such as the carriage obstruction and the electromagnetic interference. Only rely on GNSS for positioning is often insufficient to meet practical requirements. Addressing the need of the high-precision positioning for the operational personnel at railway technical stations, this paper constructs a integrated navigation system based on RTK and IMU . It utilizes a Discrete Error State Kalman Filtering algorithm to fuse the solution data from RTK and IMU, thereby obtaining high-precision pedestrian positioning data. Compared to relying solely on GNSS positioning system, the integrated navigation system significantly improves its resistance to the interference and positioning accuracy. Experimental results demonstrate that this positioning system can achieve centimeter-level positioning in obstructed scenarios typical of railway technical stations, meeting the positioning requirements of the operational personnel at railway technical station work sites.
Authors
- Xiangyu Hu (ORCID: https://orcid.org/0000-0003-0932-6659)
- Gang Xiong (ORCID: https://orcid.org/0000-0002-4303-5559)
- Fan Yang (ORCID: https://orcid.org/0000-0002-4856-1929)
- Kejie Xu
- Zhixue Wang (ORCID: https://orcid.org/0009-0009-6296-6146)
- Shichao Chen (ORCID: https://orcid.org/0000-0001-7677-4211)
Institutions
- Beijing Technology and Business University (CN)
- China Mobile (China) (CN)
- Chinese Academy of Sciences (CN)
- Shandong Institute of Automation (CN)
- Shandong Jiaotong University (CN)
- Institute of Automation (CN)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1109/dtpi61353.2024.10778763
- Primary Topic
- IoT and GPS-based Vehicle Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Research and Development