A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization
Source abstract
Abstract
Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Railway Engineering and Dynamics · Indoor and Outdoor Localization Technologies · Inertial Sensor and Navigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China
School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, China
Key Laboratory of Ocean Renewable Energy Equipment of Fujian Province, Xiamen 361021, China
Key Laboratory of Energy Cleaning Utilization and Development of Fujian Province, Xiamen 361021, China
Funding
Fujian Province Young and Middle-aged Teacher Education Research Project (Science and Technology) Key Project · China scholarship council (CSC) · National Key R&D Program of China
References (31)
Show source-provided references
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