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

¹ University of Applied Sciences „Hrvatsko Zagorje Krapina“, Krapina, Croatia
² Virovitica University of Applied Sciences, Virovitica, Croatia
³ University of Rijeka, Rijeka, Croatia

DOI: 10.12716/1001.19.01.11

Previous paper - Contents - Next paper

ABSTRACT: 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.

KEYWORDS: Risk Assessment, Accuracy, Positioning, Navigation and Timing (PNT), GNSS, Positioning, Statistical Analysis, Ionosphere, Space Weather

REFERENCES

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 - doi:10.1214/aoms/1177704477

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

Cramér, H. (1928). On the Composition of Elementary Errors. Scandinavian Actuarial Journal, 1928 (1), 13–74. doi: 10.1080/03461238.1928.10416862 - doi:10.1080/03461238.1928.10416862

Davies, K. (1990). Ionospheric Radio. Peter Peregrinus Ltd. London, UK. - doi:10.1049/PBEW031E

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

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/

Giorgino, T. (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1–24. - doi:10.18637/jss.v031.i07

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

GPS Spoofing Workgroup. (2024). GPS Spoofing: Final Report of the GPS Spoofing Workgroup. OPSGROUP. Available at: https://ops.group/blog/gps-spoofing-final-report/

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

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/

Malić E., Sikirica N., Špoljar D., Filjar R.: A Method and a Model for Risk Assessment of GNSS Utilisation with a Proof-of-Principle Demonstration for Polar GNSS Maritime Applications. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 17, No. 1, doi:10.12716/1001.17.01.03, pp. 43-50, 2023

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/

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

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

Smith, J et al. (2024). Mapping the ionosphere with millions of phones. Nature, 635, 365-369. doi: - doi:10.1038/s41586-024-08072-x

Takasu, T, rtklibexplorer. (2024.). RTKLIB Software-Defined Radio GNSS Receiver, revision demo5 b34k. Available at: https://github.com/rtklibexplorer/RTKLIB/releases

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

Citation note:

Sikirica N., Hedji I., Mikša M., Brčić D., Ciriković E., Filjar R.: An Assessment of Long-term Spatial Agnosticism of GNSS Positioning Degradation Risks Due to Ionospheric Conditions. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 19, No. 1, doi:10.12716/1001.19.01.11, pp. 85-89, 2025

BibTeX EndNote

Authors in other databases:

Nenad Sikirica: ORCID iD icon

orcid.org/0000-0002-8222-9110 Scopus icon