Application of Least-Squares Variance Component Estimation to GPS Observables
Publication: Journal of Surveying Engineering
Volume 135, Issue 4
Abstract
This contribution can be seen as an attempt to apply a rigorous method for variance components in a straightforward manner directly to GPS observables. Least-squares variance component estimation is adopted to assess the noise characteristics of GPS observables using the geometry-free observation model. The method can be applied to GPS observables or GNSS observables in general, even when the navigation message is not available. A realistic stochastic model of GPS observables takes into account the individual variances of different observation types, the satellite elevation dependence of GPS observables precision, the correlation between different observation types, and the time correlation of the observables. The mathematical formulation of all such issues is presented. The numerical evidence, obtained from real GPS data, consequently concludes that these are important issues in order to properly construct the covariance matrix of the GPS observables. Satellite elevation dependence of variance is found to be significant, for which a comparison is made with the existing elevation-dependent models. The results also indicate that the correlation between observation types is significant. A positive correlation of 0.8 is still observed between the phase observations on L1 and L2.
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Acknowledgments
The writers would like to acknowledge Professor T. Soler, the editor-in-chief, and three anonymous reviewers for their useful comments, which significantly improved the quality and presentation of this paper. The Australian Research Council Federation Fellowship support of the second writer (project number FF0883188) is greatly appreciated.
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Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 135 • Issue 4 • November 2009
Pages: 149 - 160
Copyright
© 2009 ASCE.
History
Received: Jan 10, 2008
Accepted: May 11, 2009
Published online: Oct 15, 2009
Published in print: Nov 2009
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