Research on Train Wheel Diameter Correction Based on Multi - sensor Fusion and Gray - Scale Prediction

DOI

10.2991/acaai-18.2018.9How to use a DOI?

Keywords

multi-sensor; train positioning; gray prediction; Kalman filtering

Abstract

Multi-sensor fusion is an effective way to realize low-cost and high-precision positioning on train. And Kalman filter algorithm is easy to be applied in the computer, so it is one of the important research directions of information fusion. This paper studies on the odometer in multi-sensor fusion system and analysis of its positioning error source. Based on the fuzzy adaptive Kalman filter algorithm, the gray level prediction model is introduced and improved, and the two methods are combined to improve the real-time and autonomy of the wheel diameter correction work. Verification of measured data and results of simulation demonstrate that the proposed algorithm provides high precision, high system efficiency and improved independent performance and it is of some value in application.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Teng Zhong
AU  - Shenghua Dai
PY  - 2018/03
DA  - 2018/03
TI  - Research on Train Wheel Diameter Correction Based on Multi - sensor Fusion and Gray - Scale Prediction
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 33
EP  - 36
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.9
DO  - 10.2991/acaai-18.2018.9
ID  - Zhong2018/03
ER  -