An innovative localisation algorithm for railway vehicles

Abstract

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.

A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems

“…The combination of sensors should be chosen in a way which optimizes coverage of multiple aspects, including availability, reliability, speed of response, cost, and accuracy of the system, besides providing a better on-board measurement system. For instance, integrating tachometer and transponder sensors, a type of primitive sensor fusion, will increase the accuracy level by re-initiating the position at some fixed points, but it still does not overcome errors caused by slip and slide between two transponders [123]. A localisation algorithm for increasing the accuracy of the odometric estimation, especially in critical adhesion conditions, based on sensor fusion between tachometers and inertial measurement unit, is suggested in [84].…”

High Accuracy High Integrity Train Positioning based on GNSS and Image Processing Integration

“…However in ERTMS the GNSS based estimation of the train location is currently decoupled from odometry. To properly handle slipping and sliding phenomena, advanced odometry jointly processes tightly coupled mechanical odometers and inertial sensor packages [2]. Currently, the objective is to reduce the signaling cost while maximizing the train traffic through the virtualization of the signaling equipment.…”