Selected applications of satellite technologies in rail transport

Marcin Chrzan; Tomasz Ciszewski; Waldemar Nowakowski

doi:10.61089/aot2024.z1bfx011

Authors

Marcin Chrzan Faculty of Transport, Electrical Engineering and Information Technology, Kazimierz Pulaski University in Radom, Radom, Poland Author https://orcid.org/0000-0003-4181-4847
Tomasz Ciszewski Faculty of Transport, Electrical Engineering and Information Technology, Kazimierz Pulaski University in Radom, Radom, Poland Author https://orcid.org/0000-0002-5493-2116
Waldemar Nowakowski Faculty of Transport, Electrical Engineering and Information Technology, Kazimierz Pulaski University in Radom, Radom, Poland Author https://orcid.org/0000-0003-0592-9065

DOI:

https://doi.org/10.61089/aot2024.z1bfx011

Keywords:

GNSS, Kalman filter, railway transport, automatic train operation, determining track spatial orientation, determination of defects location in rails, train integrity control

Abstract

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

References

1. Aleš, F. (2020) Certification of EGNOS Safety-of-Life Service for ERTMS According to IEC 61508 and EN 50129. Computers in Railways XVII: Railway Engineering Design and Operation, 199: 115-126, https://doi.org/10.2495/CR200111.

2. Beugin, J., Legrand, C., Marais, J., et al. (2018) Safety Appraisal of GNSS-Based Localization Systems Used in Train Spacing Control. IEEE Xplore, 6 (99): 9898 – 9916, https://doi.org/10.1109/ACCESS.2018.2807127.

3. Boffi, G., Wieser, A. (2016) Dynamics-based system noise adaption of an extended Kalman Filter for GNSS-only kinematic processing. Proceedings of the 29th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, USA, September 12-16, 2016, pp. 554–563, https://doi.org/10.33012/2016.14686.

4. Cai, B., Wu, B., Lu, D. (2020) Survey of Performance Evaluation Standardization and Research Methods on GNSS-Based Localization for Railways. Chinese Journal of Electronics, 29(1): 22-33, https://doi.org/10.1049/cje.2019.09.003.

5. Chrzan, M., Makowski, M., Ciszewski, T., et al. (2019) GNSS Based Method of train integrity control. Archives of Transport System Telematics, 12(1): 3-8.

6. Ciszewski, T. (2007) Correlation method of calibrating multi-channel ultrasonic flow detector for the automatic testing rails and wheels. Prace Naukowe Transport, 61: 97-108.

7. Duan, G., Lu, D., Dong, H., et al. (2022) GNSS-based End-of-Train Device Performance Evalua-tion and Improvement by Means of Particle Filter Algorithm. 2022 International Conference on Electromagnetics in Advanced Applications (ICEAA), Cape Town, South Africa, 2022, IEEE Xplore, pp. 101-107, https://doi.org/10.1109/ICEAA49419.2022.9899946.

8. Elmezayen, A., El-Rabbany, A. (2021). Real-time GNSS precise point positioning using improved robust adaptive Kalman filter. Survey Review, 53(381): 528–542, https://doi.org/10.1080/00396265.2020.1846361.

9. Faruqi, I., Waluya, M., Nazaruddin, Y., et al. (2018) Train Localization using Unscented Kalman Filter – Based Sensor Fusion, International Journal of Sustainable Transportation Technology, 1: 35-41, https://doi.org/10.31427/IJSTT.2018.1.2.1.

10. Flammini, F., Marrone, S., Chen, L. (2022) Formal Modelling and Optimal Traffic Management for Future Railways, Ercim News. Research and Innovation,129: 45-46. ISNN 0926-4981

11. Greiff, M., Berntorp, K., Cairano, S.D., et al. (2021) Mixed-Integer Linear Regression Kalman Filters for GNSS Positioning. IEEE Conference on Control Technology and Applications (CCTA), San Diego, CA, USA, 2021, pp. 980-985, https://doi.org/10.1109/CCTA48906.2021.9659142.

12. Gurník, P. (2016) Next Generation Train Control (NGTC): More Effective Railways through the Convergence of Main-line and Urban Train Control Systems. Transportation Research Procedia, 14: 1855-1864, https://doi.org/10.1016/j.trpro.2016.05.152.

13. Ji, K., Chai, L., Li, S., et al. (2021) Collision Risk Evaluation and Verification of GNSS-Based Train Integrity Detection. Applied Science, 2021, 11(16), 7764, https://doi.org/10.3390/app11167764.

14. Jia, M., Kassas, Z.M. (2022) Kalman Filter-Based Integrity Monitoring for GNSS and 5G Signals of Opportunity Integrated Navigation. IFAC-PapersOnLine, 55(24): 273-278. https://doi.org/10.1016/j.ifacol.2022.10.296.

15. Jon, O., Alfonso, B., Iban, L., et al. (2017) A survey of train positioning solutions. IEEE Sensors Journal, 17(20): 6788-6797, https://doi.org/10.1109/JSEN.2017.2747137.

16. Kaniewski, P. (2020) Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models. Sensors, 20(6), 1584, https://doi.org/10.3390/s20061584.

17. Kornaszewski, M., Chrzan, M., Olczykowski, Z. (2017) Implementation of New Solutions of Intel-ligent Transport Systems in Railway Transport in Poland. In: Mikulski, J. (eds) Smart Solutions in Today’s Transport. TST 2017. Communications in Computer and Information Science, 715. Springer, Cham. https://doi.org/10.1007/978-3-319-66251-0_23.

18. Liu, J., Li, J.C., Cai, B.G., et al. (2020) Test and Evaluation of GNSS-based Railway Train Position-ing under Jamming Conditions. In Proceedings of the 2020 IEEE International Conference on Sys-tems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020, pp. 1459–1464, https://doi.org/10.1109/SMC42975.2020.9283279.

19. Losada-Martin, C., Gonzalez-Fernandez, F.J. (2015) Life cycle cost matters: The axle counter revolution. DYNA, 90(2): 132-138, https://doi.org/10.6036/7272.

20. Lu, D., Schnieder, E. (2015) Performance evaluation of GNSS for train localization. IEEE Transac-tions on Intelligent Transportation Systems, 16(2): 1054–1059, https://doi.org/10.1109/TITS.2014.2349353.

21. Lu, D., Tan, J., Cai, B.G., et al. (2019) GNSS Signal in Railway Train Operation Scenario Quality Grid Generation Method. Proceedings of the ION 2019 Pacific PNT Meeting, Honolulu, Hawaii, USA, April 8 - 11, 2019 pp. 529-539, https://doi.org/10.33012/2019.16822.

22. Lu, D., Tang D., Spiegel, D. (2020) Hazard Rate Estimation for GNSS-Based Train Localization Using Model-Based Approach. Chinese Journal of Electronics, 29(1): 49-56, https://doi.org/10.1049/cje.2019.09.006.

23. Makowski, M., Chrzan, M., Bojarczak, P. (2016) Koncepcja kontroli ciągłości składu pociągu. Autobusy. Technika, Eksploatacja, Systemy Transportowe. 6: 268-270.

24. Marais, J., Beugin, J., Berbineau, M. (2017) A Survey of GNSS-Based Research and Developments for the European Railway Signaling. IEEE Transactions on Intelligent Transportation Systems. 18(10): 2602-2618, https://doi.org/10.1109/TITS.2017.2658179.

25. Marais, J., Hmida, R., Flancquart, A., et al. (2018) Video-based Classification of Railway Track Areas for GNSS-based Virtual Balise Solutions in the ERSAT GGC Project, Proceedings of the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, January 2018, pp. 196-205, https://doi.org/10.33012/2018.15594.

26. Mrohs, E.P., Machiraju, S.K.S., Aichinger-Rosenberger, M. (2021) Qualification of GNSS in Rail-way Environment, Proceedings of the 34th International Technical Meeting of the Satellite Divi-sion of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 772-777, https://doi.org/10.33012/2021.17924.

27. Neri, A. (2020) Train control and rail traffic management systems. Position, Navigation, and Tim-ing Technologies in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications, 2: 1811-1838, https://doi.org/10.1002/9781119458555.ch59.

28. Nowakowski, W., Ciszewski, T., Lukasik, Z. (2017) The Concept of Railway Traffic Control Sys-tems Remote Diagnostic. In: Mikulski, J. (eds) Smart Solutions in Today’s Transport. TST 2017. Communications in Computer and Information Science, 715. Springer, Cham, https://doi.org/10.1007/978-3-319-66251-0_38.

29. Pan, D., Luo, Q., Zhao, L., et al. (2018) A New Calibration Method for the Real-Time Calculation of Dynamic Safety Following Distance under Railway Moving Block System. Mathematical Prob-lems in Engineering, 2018: 1-11, https://doi.org/10.1155/2018/3061034.

30. Presti, L.L., Franzese, G. (2018) GNSS for Rail Transportation: Challenges and Opportunities. PoliTo Springer Series. Springer, https://doi.org/10.1007/978-3-319-79084-8.

31. Rispoli, F. (2017) GNSS for Train Control Systems: showcase ERSAT EAV and RHINOS projects. Conference: EU – Japan GNSS Mission: Tokyo

32. Sassi, I., Iovino, S., Ricevuto, N., et al. (2022) Onboard Train Integrity as enabler for ETCS Level 3. Conference: World Congress on Railway Research (WCRR) 2022: Birmingham UK

33. Sever, M., Hajiyev, Ch. (2022) GNSS-based position estimation with Kalman filters. International Journal of Sustainable Aviation (IJSA), 8(3): 181-197, https://doi.org/10.1504/IJSA.2022.124486.

34. Shuai, W.Y., Dong, X.R., Wang, J. et al. (2020) RAMS research and evaluation of GNSS for train positioning. Journal of the China Railway Society, 42(6): 70-78.

35. Spinsante, S., Stallo, C. (2020) Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty. Sensors, 20(7): 1885, https://doi.org/10.3390/s20071885.

36. Stamm, B., Gurník, P., Stamm, B. (2017) STARS project-Satellite technology for advanced railway signalling. Downloaded: https://www.stars-rail.eu/wp-content/uploads/2017/05/STARS-project-Satellite-technology-for-advanced-railway-signalling.pdf (30.04.2024)

37. Truong Ngoc, T., Khenchaf, A., Comblet, F. (2019) Evaluating Process and Measurement Noise in Extended Kalman Filter for GNSS Position Accuracy. Proceedings of 13th European Conference on Antennas and Propagation (EuCAP), Krakow, March 31-April 5, 2019, pp. 1-5.

38. U.S. Dept. of Defense. 5th ed. (2020) Global Positioning System Standard Positioning Service Performance Standard.

39. Wang, G., Han, Y., Chen, J., et al. (2018) A GNSS/INS Integrated Navigation Algorithm Based on Kalman Filter. IFAC-PapersOnLine, 51(17): 232-237, https://doi.org/10.1016/j.ifacol.2018.08.151.

40. Wu, X., Su, Z., Li, L., et al. (2023) Improved adaptive federated Kalman filtering for INS/GNSS/VNS integrated navigation algorithm. Applied Sciences, 13(9): 5790, https://doi.org/10.3390/app13095790.

41. Yuan, Y., Li, F., Chen, J., et al. (2024) An improved Kalman filter algorithm for tightly GNSS/INS integrated navigation system. Mathematical Biosciences and Engineering, 21(1): 963-983, https://doi.org/10.3934/mbe.2024040