eprintid: 10197711 rev_number: 11 eprint_status: archive userid: 699 dir: disk0/10/19/77/11 datestamp: 2024-10-11 08:13:48 lastmod: 2024-10-11 08:13:48 status_changed: 2024-10-11 08:13:48 type: thesis metadata_visibility: show sword_depositor: 699 creators_name: Sengupta, Monish title: Location and speed estimation for telematic signalling in railways ispublished: unpub divisions: UCL divisions: B04 divisions: F44 keywords: Railway, sensor fusion, ERTMS, CBTC, Kalman filter note: Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. abstract: This study considers the relationship between railway capacity and signalling systems. To increase railway capacity, signalling has been increased from two to four aspects, which can accommodate a maximum of 35 trains per hour. Although aspect signalling relies on fixed block control, the introduction of a moving block signalling system could achieve a capacity of 44 trains per hour. Achieving this would involve the introduction of telematic signalling systems (ERTMS and CBTC), which depend on the continuous and accurate measurement of the train location and speed. The current stopping accuracy required for a train is ± 1m on Britain’s mainline railway and ± 0.5m for London Underground. This is attributed mostly to the sensor fusion framework that is currently in place to obtain accurate train location and speed. In this thesis, a framework is developed based on the Kalman filter. This research shows that a linear Kalman filter fusing data from railway sensors such as Doppler radar, tachometers and wayside balise can provide good estimates of train location and speed. However, an unscented Kalman filter is capable of achieving more than four times better accuracy. The performance of these filters is investigated in the presence of wheel slip and slide, and instances of missing balise, leading to reduced capacity. In order to counter this, two adaptive sensor fusion frameworks are developed based on Magill’s filter bank and innovation tracking. Both are applied to linear and unscented Kalman filters which shows improved state estimation and stopping accuracy, hence capacity. Analysis shows that balise placement on the approach to the stopping point is important. Numerical results show that interception of a single balise reduces the location error well within the required range. Therefore, a maximum of two balises will serve the purpose with one as a backup. date: 2024-09-28 date_type: published oa_status: green full_text_type: other thesis_class: doctoral_open thesis_award: Ph.D language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2322808 lyricists_name: Sengupta, Monish lyricists_id: MSENG60 actors_name: Sengupta, Monish actors_id: MSENG60 actors_role: owner full_text_status: public pagerange: 1-312 pages: 312 institution: UCL (University College London) department: Civil, Environmental & Geomatic Engineering thesis_type: Doctoral editors_name: Heydecker, Benjamin citation: Sengupta, Monish; (2024) Location and speed estimation for telematic signalling in railways. Doctoral thesis (Ph.D), UCL (University College London). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10197711/1/Sengupta_10197711_Thesis.pdf