Spectral analysis of multi-year GNSS code multipath time-series
Jacek Kudrys
jkudrys@agh.edu.plFaculty of Mining Surveying and Environmental Engineering; AGH University of Science and Technology; (Poland)
https://orcid.org/0000-0002-4986-7436
Abstract
In the presented study multi-year time series of changes in the L1 pseudo-range multipath are analysed. Data from 8 stations of the EUREF Permanent Network (EPN) were used in the study. Periodic components present in the signal and their stability over time were analysed. Also, the type of background noise was determined, based on the spectral index. In some cases, the presence of weak components with a 1/2 and 1/3 of the Chandler period has also been found. Time-frequency analysis shows that periodic signals are not stationary in most of the examined cases, and particular signal components occur only temporarily. The analysed signals were characterised by pink noise in the lower frequency range and by white noise for higher frequencies, which is also characteristic for time series of coordinates obtained from GNSS measurements.
Keywords:
GNSS, code multipath, spectral analysis, spectral indexReferences
Yang, C., & Porter, A., “Frequency-domain characterization of GPS multipath for estimation and mitigation”, in Proceedings of the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation, ION GNSS 2005, 2005, pp. 2104–2118.
Google Scholar
Kong, X., “GPS modeling in frequency domain”, in The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, AusWireless 2007. https://doi.org/10.1109/AUSWIRELESS.2007.36
DOI: https://doi.org/10.1109/AUSWIRELESS.2007.36
Google Scholar
Zuo, X., Bu, J., Li, X., Chang, J., & Li, X., “The quality analysis of GNSS satellite positioning data”, Cluster Computing, 22, 2019, pp. 6693–6708. https://doi.org/10.1007/s10586-018-2524-1
DOI: https://doi.org/10.1007/s10586-018-2524-1
Google Scholar
Breivik, K., Forssell, B., Kee, C., Enge, P., & Walter, T., “Estimation of multipath error in GPS pseudorange measurements”. Navigation, Journal of the Institute of Navigation, vol. 44(1), 2019, pp.43–52. https://doi.or/10.1002/j.2161-4296.1997.tb01938.x
DOI: https://doi.org/10.1002/j.2161-4296.1997.tb01938.x
Google Scholar
Rost, C., & Wanninger, L. “Carrier phase multipath mitigation based on GNSS signal quality measurements”, Journal of Applied Geodesy, 3(2), 2009. https://doi.org/10.1515/jag.2009.009
DOI: https://doi.org/10.1515/JAG.2009.009
Google Scholar
Axelrad, P., Comp, C., & MacDoran, P., “Use of signal-to-noise ratio for multipath error correction in GPS differential phase measurements: methodology and experimental results”, in Proceedings of ION GPS, 1, 1994, pp. 655–666.
Google Scholar
Bilich, A., & Larson, K. M., “Mapping the GPS multipath environment using the signal-to-noise ratio (SNR)”. Radio Science, 42(6), 2007. https://doi.org/10.1029/2007RS003652
DOI: https://doi.org/10.1029/2007RS003652
Google Scholar
Yu, K., Ban, W., Zhang, X., & Yu, X., “Snow depth estimation based on multipath phase combination of GPS triple-frequency signals”. IEEE Transactions on Geoscience and Remote Sensing, vol. 53(9), 2015, pp. 5100–5109. https://doi.org/10.1109/TGRS.2015.2417214
DOI: https://doi.org/10.1109/TGRS.2015.2417214
Google Scholar
Komjathy, A., Armatys, M., Masters, D., Axelrad, P., Zavorotny, V., & Katzberg, S., “Retrieval of ocean surface wind speed and wind direction using reflected GPS signals”, Journal of Atmospheric and Oceanic Technology, vol. 21(3), 2004, pp. 515–526. https://doi.org/10.1175/1520-0426(2004)021<0515:ROOSWS>2.0.CO;2
DOI: https://doi.org/10.1175/1520-0426(2004)021<0515:ROOSWS>2.0.CO;2
Google Scholar
Kim, S. K., & Park, J., “Monitoring sea level change in arctic using GNSS-reflectometry”, in ION 2019 International Technical Meeting Proceedings, 2019, pp. 665–675. https://doi.org/10.33012/2019.16717
DOI: https://doi.org/10.33012/2019.16717
Google Scholar
Chang, X., Jin, T., Yu, K., Li, Y., Li, J., & Zhang, Q., “Soil moisture estimation by GNSS multipath signal”. Remote Sensing, vol. 11, 1st November 2019. https://doi.org/10.3390/rs11212559
DOI: https://doi.org/10.3390/rs11212559
Google Scholar
“EUREF Permanent GNSS Network”. Available: http://epncb.eu/ [Accessed: 05 Jan 2020]
Google Scholar
“International GNSS Service”. Available: http://www.igs.org/ [Accessed: 05 Jan 2020]
Google Scholar
Vaclavovic, P., & Dousa, J., “G-Nut/Anubis – open-source tool for multi-GNSS data monitoring”, in: IAG Symposia Series, Springer, vol. 143, 2016, pp. 775-782. https://doi.org/10.1007/1345_2015_157
DOI: https://doi.org/10.1007/1345_2015_157
Google Scholar
Estey, L. H., & Meertens, C. M., “TEQC: The Multi-Purpose Toolkit for GPS/GLONASS Data”, GPS Solutions, 3(1),1999, pp. 42–49. https://doi.org/10.1007/PL00012778
DOI: https://doi.org/10.1007/PL00012778
Google Scholar
Lomb, N. R., “Least-squares frequency analysis of unequally spaced data”. Astrophysics and Space Science, 39(2), 1976, pp. 447–462. https://doi.org/10.1007/BF00648343
DOI: https://doi.org/10.1007/BF00648343
Google Scholar
Scargle, J. D., “Studies in astronomical time series analysis. II – Statistical aspects of spectral analysis of unevenly spaced data”, The Astrophysical Journal, 263, 1982, p. 835. https://doi.org/10.1086/160554
DOI: https://doi.org/10.1086/160554
Google Scholar
Klos, A., Bogusz, J., Bos, M. S., & Gruszczynska, M., “Different Approaches to Extract Seasonal Signals” in Modelling the GNSS Time Series. Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-21718-1_7
DOI: https://doi.org/10.1007/978-3-030-21718-1_7
Google Scholar
Bogusz, J., & Klos, A., “On the significance of periodic signals in noise analysis of GPS station coordinates time series”. GPS Solutions, 20(4), 2016, pp. 655–664. https://doi.org/10.1007/s10291-015-0478-9
DOI: https://doi.org/10.1007/s10291-015-0478-9
Google Scholar
Ray, J. D., Vijayan, M. S. M., Godah, W., & Kumar, A., “Investigation of background noise in the GNSS position time series using spectral analysis – A case study of Nepal Himalaya”. Geodesy and Cartography, 68(2), 2019, pp. 375–388. https://doi.org/10.24425/gac.2019.128468
Google Scholar
Torrence Christopher, & P. Compo Gilbert., “A Practical Guide to Wavelet Analysis”. Bulletin of the American Meteorological Society, 137(2), 1998, pp. 87–92. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
DOI: https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
Google Scholar
Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux, “The NumPy Array: A Structure for
Google Scholar
Efficient Numerical Computation”, Computing in Science & Engineering, 13, 2011, pp. 22-30. https://doi.org/10.1109/MCSE.2011.37
DOI: https://doi.org/10.1109/MCSE.2011.37
Google Scholar
Wes McKinney, “Data Structures for Statistical Computing in Python”, in Proceedings of the 9th Python in Science Conference, 2010, pp. 51-56
DOI: https://doi.org/10.25080/Majora-92bf1922-00a
Google Scholar
Lee et al., “PyWavelets: A Python package for wavelet analysis”. Journal of Open Source Software, 4 (36), 2019, p. 1237. https://doi.org/10.21105/joss.01237
DOI: https://doi.org/10.21105/joss.01237
Google Scholar
John D. Hunter, “Matplotlib: A 2D Graphics Environment”, Computing in Science & Engineering, vol. 9, 2007, pp. 90-95. https://doi.org/10.1109/MCSE.2007.55
DOI: https://doi.org/10.1109/MCSE.2007.55
Google Scholar
Authors
Jacek Kudrysjkudrys@agh.edu.pl
Faculty of Mining Surveying and Environmental Engineering; AGH University of Science and Technology; Poland
https://orcid.org/0000-0002-4986-7436
Statistics
Abstract views: 373PDF downloads: 214
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Budownictwo i Architektura supports the open science program. The journal enables Open Access to their publications. Everyone can view, download and forward articles, provided that the terms of the license are respected.
Publishing of articles is possible after submitting a signed statement on the transfer of a license to the Journal.
