Noise in multivariate GPS position time-series |
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| Authors: | A R Amiri-Simkooei |
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| Institution: | (1) Delft Institute of Earth Observation and Space Systems (DEOS), Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands;(2) Department of Surveying Engineering, Faculty of Engineering, The University of Isfahan, 81744 Isfahan, Iran |
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| Abstract: | A methodology is developed to analyze a multivariate linear model, which occurs in many geodetic and geophysical applications.
Proper analysis of multivariate GPS coordinate time-series is considered to be an application. General, special, and more
practical stochastic models are adopted to assess the noise characteristics of multivariate time-series. The least-squares
variance component estimation (LS-VCE) is applied to estimate full covariance matrices among different series. For the special
model, it is shown that the multivariate time-series can be estimated separately, and that the (cross) correlation between
series propagates directly into the correlation between the corresponding parameters in the functional model. The time-series
of five permanent GPS stations are used to show how the correlation between series propagates into the site velocities. The
results subsequently conclude that the general model is close to the more practical model, for which an iterative algorithm
is presented. The results also indicate that the correlation between series of different coordinate components per station
is not significant. However, the spatial correlation between different stations for individual components is significant (a
correlation of 0.9 over short baselines) both for white and for colored noise components. |
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| Keywords: | Least-squares variance component estimation (LS-VCE) Normal distribution Multivariate GPS time-series Spatial correlation |
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