Abstract
Navigation system for high-speed trains is necessary for increased operational safety and efficiency, new services for customers, and low maintenance cost. This paper proposes a high accuracy navigation system for high-speed trains based on a sensor fusion algorithm, with non-holonomic constraints, for multiple sensors, such as accelerometers, gyroscopes, tachometers, Doppler radar, differential GPS, and RFID, and a map matching algorithm. In the proposed system, we consider the federated Kalman filter for sensor fusion, where local filters utilize filter models developed for various sensor types. Especially, the local Kalman filter for RFID positioning, that is detected at irregular time intervals due to the varying train speed and RFID tag spacing, is developed to maintain high performance during GPS outage. In addition, an orthogonal projection map matching algorithm is developed to improve the performance of the proposed system. The performance of the proposed system is demonstrated with numerous simulations for a high-speed train in Korea. The simulation results are analyzed with respect to the existence of tunnel, RFID deployment spacing, RFID location uncertainty, and DGPS error.
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Kwanghoon Kim received his Ph.D. degree in Electrical Engineering and Computer Science from the Seoul National University in 2006. He is currently working as a research professor at the CCS Graduate School for Green Transportation, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. His research interests include Fault Detection, Kalman Filtering, GNSS/INS integration system, and GNSS signal processing algorithms.
Sanghwan Seol received his B.S. degree in Mechanical Engineering in 2012 and his M.S. degree from the CCS Graduate School for Green Transportation in 2014, both in the Korea Advanced Institute of Science and Technology (KAIST). He is currently a research engineer at the Raybolt System PMO, Agency for Defense Development (ADD). His research interests include optimal control, detection and estimation for navigation systems.
Seung-Hyun Kong received his B.S. degree in Electronics Engineering from the Sogang University, Korea, in 1992, an M.S. degree in Electrical Engineering from the Polytechnique University, New York, in 1994, and a Ph.D. degree in Aeronautics and Astronautics from the Stanford University, CA, in Jan., 2006. From 1997 to 2004, he was with Samsung Electronics Inc., and Nexpilot Inc., both in Korea, where his research focus was on 2G CDMA and 3G UMTS PHY and mobile positioning technologies. In 2006, he was involved with hybrid positioning technology development using wireless location signature and Assisted GNSS at Polaris Wireless Inc., CA., and from 1997 to 2009, he was a research staff at the corp. R&D, Qualcomm Inc., CA, where his R&D focus was on the indoor location technologies and advanced GNSS technologies. He joined Korea Advanced Institute of Science and Technology (KAIST) in 2010, and he is an associate professor at the CCS Graduate School for Green Transportation at the same institution. His research interests include super-resolution signal processing, detection and estimation for navigation systems, and assisted GNSS in wireless communication systems.
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Kim, K., Seol, S. & Kong, SH. High-speed train navigation system based on multi-sensor data fusion and map matching algorithm. Int. J. Control Autom. Syst. 13, 503–512 (2015). https://doi.org/10.1007/s12555-014-0251-9
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DOI: https://doi.org/10.1007/s12555-014-0251-9