Time-Correlated GPS Noise Dependency on Data Time Period

  • Alvaro Santamaría-Gómez
  • Marie-Noëlle Bouin
  • Xavier Collilieux
  • Guy Wöppelmann
Conference paper
Part of the International Association of Geodesy Symposia book series (IAG SYMPOSIA, volume 138)

Abstract

GPS position time series contain time-correlated noise. The estimated parameters using correlated time series data, as station velocities, are then more uncertain than if the time series data were uncorrelated. If the level of the time-correlated noise is not taken into account, the estimated formal uncertainties will be smaller. By estimating the type and amplitude of the noise content in time series, more realistic formal uncertainties can be assessed.

However, time-correlated noise amplitude is not constant in long time series, but depends on the time period of the time series data. Older time series data contain larger time-correlated noise amplitudes than newer time series data. This way, shorter time series with older data time period exhibit time-correlated noise amplitudes similar to the whole time series. This paper focuses on the source of the time-correlated noise amplitude decrease from older to newer time series period data. The results of several tested sources are presented. Neither the increasing ambiguity fixation rate, nor the increasing number of tracking stations, nor the increasing number of observed satellites are likely the source of the noise reduction. The quality improvement of the equipment of both tracking network and constellation is likely the main source of the correlated noise evolution.

Keywords

GPS Time series Time-correlated noise 

References

  1. Altamimi Z, Collilieux X, Métivier L (2011) ITRF2008 : an improved 280 solution of the international terrestrial frame. J Geodesy 85:457–473, doi:10.1007/s00190-011-0444-4 Google Scholar
  2. Beavan J (2005) Noise properties of continuous GPS data from concrete pillar geodetic monuments in New Zealand and comparison with data from U.S. deep drilled braced monuments. J Geophys Res 110:B08410. doi:10.1029/2005JB003642 CrossRefGoogle Scholar
  3. Boehm J, Werl B, Schuh H (2006) Troposphere mapping functions for GPS and very long baseline interferometry from European centre for medium-range weather forecasts operational analysis data. J Geophys Res 111:B02406. doi:10.1029/2005JB003629 CrossRefGoogle Scholar
  4. Bos MS, Bastos L, Fernandes RMS (2010) The influence of seasonal signals on the estimation of the tectonic motion in short continuous GPS time-series. J Geodyn 49:205–209. doi:10.1016/j.jog.2009.10.005 CrossRefGoogle Scholar
  5. Herring TA, King RW, McClusky SC (2008) Introduction to GAMIT/GLOBK, report, mass. Institute of Technology, CambridgeGoogle Scholar
  6. King MA, Watson CS (2010) Long GPS coordinate time series: multipath and geometry effects. J Geophys Res 115:B04403. doi:10.1029/2009JB006543 CrossRefGoogle Scholar
  7. King MA, Williams SDP (2009) Apparent stability of GPS monumentation from shortbaseline time series. J Geophys Res 114:B10403. doi:10.1029/2009JB006319 CrossRefGoogle Scholar
  8. Kouba J (2007) Implementation and testing of the gridded Vienna mapping function 1 (VMF1). J Geodesy. doi:10.1007/s00190-007-0170-3
  9. Lyard F, Lefevre F, Letellier T, Francis O (2006) Modelling the global ocean tides: modern insights from FES2004. Ocean Dynam 56:394–415. doi:10.1007/s10236-006-0086-x CrossRefGoogle Scholar
  10. Mao A, Harrison CGA, Dixon TH (1999) Noise in GPS coordinate time series. J Geophys Res 104(B2):2797–2816CrossRefGoogle Scholar
  11. Santamaría-Gómez A, Bouin M-N, Collilieux X, Wöppelmann G (2011) Correlated errors in GPS position time series: implications for velocity estimates. J Geophys Res 116:B01405. doi:10.1029/2010JB007701 CrossRefGoogle Scholar
  12. Santamaría-Gómez A, Bouin M-N, Wöppelmann G (2012) Improved GPS data analysis strategy for tide gauge benchmark monitoring. In: S. Kenyon, M.C. Pacino, and U. Marti (eds.), Proceedings of the 2009 IAG Symposium, Buenos Aires, Argentina, 31 August–4 September 2009, doi:10.1007/978-3-642-20338-1_2
  13. Schmid R, Steigenberger P, Gendt G, Ge M, Rothacher M (2007) Generation of a consistent absolute phase-center correction model for GPS receiver and satellite antennas. J Geodesy 81:781–798. doi:10.1007/s00190-007-0148-y CrossRefGoogle Scholar
  14. Tregoning P, Watson C (2009) Atmospheric effects and spurious signals in GPS analyses. J Geophys Res 114:B09403. doi:10.1029/2009JB006344 CrossRefGoogle Scholar
  15. Williams SDP (2003a) The effect of colored noise on the uncertainties of rates estimated from geodetic time series. J Geodesy 76:483–494. doi:10.1007/s00190-002-0283-4 CrossRefGoogle Scholar
  16. Williams SDP (2003b) Offsets in global positioning system time series. J Geophys Res 108(B6):2310. doi:10.1029/2002JB002156, 2003 CrossRefGoogle Scholar
  17. Williams SDP (2008) CATS: GPS coordinate time series analysis software. GPS Solut 12(2):147–153. doi:10.1007/s10291-007-0086-4 CrossRefGoogle Scholar
  18. Wöppelmann G, Letetrel C, Santamaría A, Bouin M-N, Collilieux X, Altamimi Z, Williams SDP, Martín Miguez B (2009) Rates of sea-level change over the past century in a geocentric reference frame. Geophys Res Lett 36:L12607. doi:10.1029/2009GL038720 CrossRefGoogle Scholar
  19. Zhang J, Bock Y, Johnson H, Fang P, Williams S, Genrich J, Wdowinski S, Behr J (1997) Southern California permanent GPS geodetic array: error analysis of daily position estimates and site velocities. J Geophys Res 102:18,035–18,055Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alvaro Santamaría-Gómez
    • 1
    • 2
  • Marie-Noëlle Bouin
    • 3
  • Xavier Collilieux
    • 2
  • Guy Wöppelmann
    • 4
  1. 1.Instituto Geográfico NacionalYebesSpain
  2. 2.IGN/LAREG, Université Paris DiderotParisFrance
  3. 3.Centre National de Recherches MétéorologiquesBrestFrance
  4. 4.Fédération de Recherche en Environnement pour le Développement Durable (FR 3097 CNRS)Université de La Rochelle/CNRSLa RochelleFrance

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