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Journal Article

Determination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data

Authors
/persons/resource/bsoja

Soja,  B.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/nilsson

Nilsson,  T.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/balidak

Balidakis,  Kyriakos
External Organizations;

/persons/resource/sglaser

Glaser,  Susanne
External Organizations;

/persons/resource/rob

Heinkelmann,  R.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/schuh

Schuh,  H.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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1731911.pdf
(Postprint), 4MB

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Citation

Soja, B., Nilsson, T., Balidakis, K., Glaser, S., Heinkelmann, R., Schuh, H. (2016): Determination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data. - Journal of Geodesy, 90, 12, 1311-1327.
https://doi.org/10.1007/s00190-016-0924-7


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_1731911
Abstract
Terrestrial reference frames (TRF), such as the ITRF2008, are primary products of geodesy. In this paper, we present TRF solutions based on Kalman filtering of very long baseline interferometry (VLBI) data, for which we estimate steady station coordinates over more than 30 years that are updated for every single VLBI session. By applying different levels of process noise, non-linear signals, such as seasonal and seismic effects, are taken into account. The corresponding stochastic model is derived site-dependent from geophysical loading deformation time series and is adapted during periods of post-seismic deformations. Our results demonstrate that the choice of stochastic process has a much smaller impact on the coordinate time series and velocities than the overall noise level. If process noise is applied, tests with and without additionally estimating seasonal signals indicate no difference between the resulting coordinate time series for periods when observational data are available. In a comparison with epoch reference frames, the Kalman filter solutions provide better short-term stability. Furthermore, we find out that the Kalman filter solutions are of similar quality when compared to a consistent least-squares solution, however, with the enhanced attribute of being easier to update as, for instance, in a post-earthquake period.