Spectral analysis of multi-year GNSS code multipath time-series

• Autorzy: Kudrys Jacek

Identyfikatory

DOI

10.35784/bud-arch.1319

• Języki publikacji: EN

Abstrakty

EN

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 char-acterised 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.

Słowa kluczowe

EN

GNSS; code multipath; spectral analysis; spectral index

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Budownictwo i Architektura
• Rocznik: 2019
• Tom: Vol. 18, nr 4
• Strony: 15--22
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Kudrys Jacek

• Faculty of Mining Surveying and Environmental Engineering;AGH University of Science and Technology

Bibliografia

• [1] 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.
• [2] 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
• [3] 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
• [4] 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.org/10.1002/j.2161-4296.1997.tb01938.x
• [5] 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
• [6] 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.
• [7] 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
• [8] Yu, K., Ban, W., Zhang, X., & Yu, X., “Snow depth estimation based on multipath phase combi - nation 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
• [9] 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
• [10] 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
• [11] 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
• [12] “EUREF Permanent GNSS Network”. Available: http://epncb.eu/ [Accessed: 05 Jan 2020]
• [13] “International GNSS Service”. Available: http://www.igs.org/ [Accessed: 05 Jan 2020]
• [14] 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
• [15] 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
• [16] 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
• [17] 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
• [18] 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
• [19] 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
• [20] 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
• [21] 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/10.1175/1520-047 7(1998)079<0061:APGTWA>2.0.CO;2
• [22] Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux, “The NumPy Array: A Structure for Efficient Numerical Computation”, Computing in Science & Engineering , 13, 2011, pp. 22-30, https://doi.org/10.1109/MCSE.2011.37
• [23] Wes McKinney, “Data Structures for Statistical Computing in Python”, in Proceedings of the 9th Python in Science Conference, 2010, pp. 51-56
• [24] 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
• [25] 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

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).