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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 110
Edited by: J. Pombo
Paper 91

Estimation of Three-Dimensional Railway Track Geometry using Nonlinear Recursive Bayesian Filtering and its Application

A. Yoshimura

School of Computer Science, Tokyo University of Technology, Japan

Full Bibliographic Reference for this paper
A. Yoshimura, "Estimation of Three-Dimensional Railway Track Geometry using Nonlinear Recursive Bayesian Filtering and its Application", in J. Pombo, (Editor), "Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 91, 2016. doi:10.4203/ccp.110.91
Keywords: track geometry reconstruction, three-point chord offset method, parametric representation, track space curve, measurement model, track banking, recursive nonlinear filtering.

In this paper, a new method for reconstructing a three-dimensional railway track geometry, considering the bank of the track due to the cant in the curve is described. The recursive nonlinear filtering based on the Bayesian approach is applied and implemented using the sequential Monte Carlo particle filter. The method makes use of the measurements of the asymmetrical three-point chord offset for level and alignment as well as cant in the track inspection car and reconstructs the track geometry consisting of both the left and right rails. By taking into account a mutual geometric dependency between the left and right rail due to the cant, more accurate reconstruction is attained. In the new method, three-kinds of curvature along the track are also estimated. These will be very useful for not only railway track maintenance and management but also the investigation of the vehicle track dynamic interaction. Examples are given.

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