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Warianty tytułu
Języki publikacji
Abstrakty
Provision of uninterrupted and robust Positioning, Navigation, and Timing (PNT) services is essential task of Global Navigation Satellite Systems (GNSS) as an enabling technology for numerous technology and socio-economic applications, a cornerstone of the modern civilisation, a public goods, and an essential component of a national infrastructure. GNSS resilience may be accomplished only with complete understanding of the causes of GNSS positioning performance disruptions and degradations, presented in a form of applications- and scenarios-related models. Here the application of change-point detection methods is proposed and demonstrated in a selected scenario of a fast-developing ionospheric storm’s impact on GNSS positioning performance, as a novel contribution to forecasting GNSS positioning performance model development and GNSS utilisation risk mitigation.
Słowa kluczowe
Rocznik
Tom
Strony
575--579
Opis fizyczny
Bibliogr. 16 poz., rys.
Bibliografia
- 1. Aminikhanghahi, S, Cook, D. (2017). A Survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339-367. Available at: https://www.eecs.wsu.edu/~cook/pubs/kais16.2.pdf - doi:10.1007/s10115-016-0987-z
- 2. Chandola, V, Vatsavai, R R. (2011). A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series. Proc of the 2011 SIAM International Conference on Data Mining, 95-106. Mesa, AZ. - doi:10.1137/1.9781611972818.9
- 3. Filić, M, Filjar, R. (2019). On correlation between SID monitor and GPS-derived TEC observations during a massive ionospheric storm development. Best Student Paper Award at URSI AP-RASC 2019. New Delhi, India. Available at: https://bit.ly/2FSJu0Y - doi:10.23919/URSIAP-RASC.2019.8738664
- 4. Filić, M, Filjar, R. (2018). Forecasting model of space weather-driven GNSS positioning performance. Lambert Academic Publishing. Riga, Latvia. ISBN 978-613-9-90118-0.
- 5. Filić M., Filjar R., Ruotsalainen L.: An SDR-based Study of Multi-GNSS Positioning Performance During Fast-developing Space Weather Storm. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 10, No. 3, doi:10.12716/1001.10.03.03, pp. 395-400, 2016
- 6. Gustafsson, F. (2000). Adaptive Filtering and Change Detection. John Wiley & Sons. Chichester, UK. - doi:10.1002/0470841613
- 7. Killick, R. (2016). R Package changepoint. R project for statistical computing. Available at: https://cran.r-project.org/web/packages/changepoint/index.html
- 8. Killick, R, Eckley, I A. (2014). Changepoint: An R Package for Changepoint Analysis. Journal of Statistical Software, 58(39, 1-19. doi: 10.18637/jss.v058.i03 - doi:10.18637/jss.v058.i03
- 9 Lenac, K, Filić, M, Filjar, R. (2019). GPS ionospheric delay dynamics characterisation using recurrence plot analysis. Presented for consideration to J of Navigation (Cambridge University Press).
- 10. NOAA. (2019). Kp index data archive. US National Oceanic and Atmospheric Administration (NOAA). Available at: ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/
- 11. Sonel. (2019). Internet archive of GPS observations. SONEL network. Available at: https://www.sonel.org
- 12. Schroeder, A L M M. (2016). Methods for Change-Point Detection with Additional Interpretability. London School of Economics and Political Sciences. London, UK. Available at: http://etheses.lse.ac.uk/3421/
- 13. Scott, A J, Knott, M. (1974). A Cluster Analysis Method for Grouping Means in the Analysis of Variance. Biometrics, 30(3), 507-512. - doi:10.2307/2529204
- 14. Truong, C, Oudre, L, Vaytis, N. (2019). Selective review of offline change point detection methods. Preprint at arXiv: 1801.00718. Available at: https://arxiv.org/pdf/1801.00718.pdf
- 15. Watanabe, S. (2013). A Widely Applicable Bayesian Information Criterion. J of Machine Learning Res, 14, 867-897. Available at: http://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf
- 16. HM Government Office for Science. (2018). Satellite-Derived Time and Position: A Study of Critical Dependencies. HM Government of the United Kingdom and Northern Ireland. Available at: https://bit.ly/2E2STnd
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-97990d34-a770-4b9a-ba21-b07931fd1520