Computational Technology Resources - CCP

A. Yoshimura, Y. Naganuma, "Bayesian Reconstruction of 3D Railway Track Geometry by Particle Filter", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 88, 2014. doi:10.4203/ccp.104.88

Keywords: reconstruction of 3D railway track geometry, three point chord versine measurements, parametric representation of 3D curve, self-organizing state-space model, local curvatures, recursive nonlinear filtering, Bayesian approach, sequential Monte Carlo particle filter.

Summary

In this paper, a new method of reconstructing three dimensional (3D) railway track geometry is described. The recursive nonlinear filtering based on the Bayesian approach is applied and implemented by the sequential Monte Carlo particle filter. Instead of reconstructing 2D planar curve independently in the vertical or horizontal direction, a new method makes use of 3-point chord versines for level and alignment simultaneously. By taking into account a mutual geometric dependency between the vertical direction and horizontal one, more accurate reconstruction becomes possible. In the new method, not only 3D railway track geometry but also local curvatures along the track in the vertical and horizontal directions are obtained. Examples are given and the effectiveness of the method is discussed.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £65 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 104 PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 88 Bayesian Reconstruction of 3D Railway Track Geometry by Particle Filter A. Yoshimura¹ and Y. Naganuma² ¹School of Computer Science, Tokyo University of Technology, Japan ²Nihon Kikai Hosen k. k., Tokyo, Japan doi:10.4203/ccp.104.88 purchase the full-text of this paper Full Bibliographic Reference for this paper A. Yoshimura, Y. Naganuma, "Bayesian Reconstruction of 3D Railway Track Geometry by Particle Filter", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 88, 2014. doi:10.4203/ccp.104.88 Keywords: reconstruction of 3D railway track geometry, three point chord versine measurements, parametric representation of 3D curve, self-organizing state-space model, local curvatures, recursive nonlinear filtering, Bayesian approach, sequential Monte Carlo particle filter. Summary In this paper, a new method of reconstructing three dimensional (3D) railway track geometry is described. The recursive nonlinear filtering based on the Bayesian approach is applied and implemented by the sequential Monte Carlo particle filter. Instead of reconstructing 2D planar curve independently in the vertical or horizontal direction, a new method makes use of 3-point chord versines for level and alignment simultaneously. By taking into account a mutual geometric dependency between the vertical direction and horizontal one, more accurate reconstruction becomes possible. In the new method, not only 3D railway track geometry but also local curvatures along the track in the vertical and horizontal directions are obtained. Examples are given and the effectiveness of the method is discussed. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £65 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions