PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

An Assessment of Long-term Spatial Agnosticism of GNSS Positioning Degradation Risks Due to Ionospheric Conditions

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The Global Navigations Satellite Systems (GNSS) have been evolved into an essential infrastructure of modern civilisation, a public goods, and enabler of rapidly growing number of technology and socio-economic applications. However, GNSS applications often lack fundamental details on GNSS Positioning, Navigation, and Timing (PNT services performance to define and determine their Quality of Service (QoS). The lack of alignment with the core GNSS PNT deprives GNSS applications of assessing the risks of the GNSS PNT utilisation, thus leaving GNSS applications unable to prepare alternatives and mitigate the causes of GNSS PNT performance disruptions. Here we contributed to solution of the problem with the introduction and long-term performance assessment of the risk model of ionospheric-caused GNSS positioning degradation. Called the Probability of Occurrence (PoO), our team defined the risk model of GNSS positioning degradation caused by ionospheric conditions based on the long term observations of occurrences of degraded GNSS positioning performance. In the process of the GNSS risk model validation, the long-term PoO risk models are developed using the annual 2014 stationary GNSS horizontal positioning error observations derived from the GNSS pseudoranges collected at the International GNSS Service (IGS) reference stations situated in polar (Iqaluit, Canada) and sub-equatorial regions (Darwin, Australia). Two GNSS risk models are compared for similarity using statistical methods of Hausdorff distance and Cramér–von Mises statistical test. Research results show that two GNSS risk models are spatially agnostic, since no significant difference in two long-term GNSS risk models is found. The research results supports the conclusion of generality of the PoO GNSS risk model, and its ability to serve GNSS applications developers, operators, and users in determination of the QoS of particular GNSS applications.
Twórcy
  • University of Aplied Sciences Hrvatsko zagorje Krapina, Krapina, Croatia
autor
  • Virovitica University of Applied Sciences, Virovitica, Croatia
autor
  • University of Aplied Sciences Hrvatsko zagorje Krapina, Krapina, Croatia
  • University of Rijeka, Rijeka, Croatia
  • Virovitica University of Applied Sciences, Virovitica, Croatia
  • University of Aplied Sciences Hrvatsko zagorje Krapina, Krapina, Croatia
  • University of Rijeka, Rijeka, Croatia
Bibliografia
  • [1] Anderson, T W. (1962). On the Distribution of the Two-Sample Cramer–von Mises Criterion. Annals of Mathematical Statistics, 33 (3), 1148–1159. doi:10.1214/aoms/1177704477
  • [2] Bonenberg, L, Motella, B, Fortuny Guasch, J. (2023). Assessing Alternative Positioning, Navigation and Timing Technologies for Potential Deployment in the EU, EUR 31450 EN. Publications Office of the European Union, Luxembourg. ISBN 978-92-68-01163-8, doi:10.2760/596229, JRC132737
  • [3] Cramér, H. (1928). On the Composition of Elementary Errors. Scandinavian Actuarial Journal, 1928 (1), 13–74. doi: 10.1080/03461238.1928.10416862
  • [4] Davies, K. (1990). Ionospheric Radio. Peter Peregrinus Ltd. London, UK.
  • [5] EUSPA. (2025). EUSPA GNSS User Needs and Requirements Library. European Agency for Spaace Programme (EUSPA). Prague, Czechia. Available at: https://www.gsc-europa.eu/electronic-library/gnss-market-and-user-reports#userneeds
  • [6] Flytkjær, R et al. (2023). The economic impact on the UK of a disruption to GNSS, final report. London Economics. London, UK. Available at: https://londoneconomics.co.uk/blog/publication/the-economic-impact-on-the-uk-of-a-disruption-to-gnss-2021/
  • [7] Giorgino, T. (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1–24. https://doi.org/10.18637/jss.v031.i07
  • [8] Goward, D. (2016). Prioritizing dangers to the United States from Threats to GPS: Ranking Risks and Porposed Mitigations. Resilient Navigation and Timing Foundation. Alexandria, VA. Available at: https://rntfnd.org/wp-content/uploads/12-7-Prioritizing-Dangers-to-US-fm-Threats-to-GPS-RNTFoundation.pdf
  • [9] GPS Spoofing Workgroup. (2024). GPS Spoofing: Final Report of the GPS Spoofing Workgroup. OPSGROUP. Available at: https://ops.group/blog/gps-spoofing-final-report/
  • [10] Henrikson, J T. (1999). Completeness and Total Boundedness of the Hausdorff Metric. MIT Undergrduate Journal of Mathematics. Available at: https://www.semanticscholar.org/paper/Completeness-and-Total-Boundedness-of-the-Hausdorff-Henrikson/91b0de0eb189355e27a26e3848d41c818c95350d
  • [11] IGS. (2025). International GNSS Service database of GNSS observations, maintained by NASA. Available at, with the required free registration: https://cddis.nasa.gov/archive/gnss/data/daily/
  • [12] Malić, E, Sikirica, N, Špoljar, D, Filjar, R. (2023). A method and a model for risk assessment of GNSS utilisation with a proof-of-principle demonstration for polar GNSS maritime applications. TransNav, 17(1), 43-50. doi: 10.12716/1001.17.01.03
  • [13] R project team. (2025). The R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Available through: https://www.r-project.org/
  • [14] Research group of Astronomy and Geomatics (gAGE). (2024). gLAB Tool Suite. gAGE, Universitat Politecnica de Catalunya (UPC). Barcelona, Spain. Available at: https://gage.upc.edu/en/learning-materials/software-tools/glab-tool-suite
  • [15] Sanz Subirana, J., Juan Zornoza, J. M., and Hernandez-Pajares, M. (2013). GNSS Data Processing – Volume I: Fundamentals and Algorithms. ESA Communications. Available at: http://bit.ly/1tDzJIQ
  • [16] Smith, J et al. (2024). Mapping the ionosphere with millions of phones. Nature, 635, 365-369. doi: https://doi.org/10.1038/s41586-024-08520-8
  • [17] Takasu, T, rtklibexplorer. (2024.). RTKLIB Software-Defined Radio GNSS Receiver, revision demo5 b34k. Available at: https://github.com/rtklibexplorer/RTKLIB/releases
  • [18] Wang, X et al. (2010). Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 2010, 1–35. doi: https://doi.org/10.48550/arXiv.1012.2789
Uwagi
1. Pełne imiona podano na stronie internetowej czasopisma w "Authors in other databases."
2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cc8bbee0-e605-4206-9a47-09ffd553fd6a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.