Journal of Geodesy

, Volume 82, Issue 3, pp 157–166 | Cite as

Fast error analysis of continuous GPS observations

  • M. S. BosEmail author
  • R. M. S. Fernandes
  • S. D. P. Williams
  • L. Bastos
Original Article


It has been generally accepted that the noise in continuous GPS observations can be well described by a power-law plus white noise model. Using maximum likelihood estimation (MLE) the numerical values of the noise model can be estimated. Current methods require calculating the data covariance matrix and inverting it, which is a significant computational burden. Analysing 10 years of daily GPS solutions of a single station can take around 2 h on a regular computer such as a PC with an AMD AthlonTM 64 X2 dual core processor. When one analyses large networks with hundreds of stations or when one analyses hourly instead of daily solutions, the long computation times becomes a problem. In case the signal only contains power-law noise, the MLE computations can be simplified to a \({\mathcal{O}}(N\log N)\) process where N is the number of observations. For the general case of power-law plus white noise, we present a modification of the MLE equations that allows us to reduce the number of computations within the algorithm from a cubic to a quadratic function of the number of observations when there are no data gaps. For time-series of three and eight years, this means in practise a reduction factor of around 35 and 84 in computation time without loss of accuracy. In addition, this modification removes the implicit assumption that there is no environment noise before the first observation. Finally, we present an analytical expression for the uncertainty of the estimated trend if the data only contains power-law noise.


GPS Power-law Correlated noise Time-series analysis 


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Supplementary material

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • M. S. Bos
    • 1
    Email author
  • R. M. S. Fernandes
    • 2
    • 3
  • S. D. P. Williams
    • 4
  • L. Bastos
    • 1
  1. 1.University of Porto, Monte da VirgemVila Nova de GaiaPortugal
  2. 2.University of Beira Interior, CGUL, IDLCovilhãPortugal
  3. 3.Delft University of Technology, DEOSDelftThe Netherlands
  4. 4.Proudman Oceanographic LaboratoryLiverpoolUK

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