Performance of a Kalman Filter in EOP determination from VLBI data: test case CONT14 M. Karbon, B. Soja, T. Nilsson, R. Heinkelmann, J. Anderson, K. Balidakis, S. Glaser* ( ) L. Liu, J.A. Mora-Diaz, M. Xu, H. Schuh * GFZ German Research Centre for Geosciences, Potsdam Germany; Contact:
[email protected] *Technische Universität Berlin, Institute for Geodesy and Geoinformation Science, Berlin Germany Introduction: Within the project VLBI-ART we developed a Kalman filter for the GFZ version of the VLBI analysis software VieVS. This method has the advantage that it is simultaneously possible to estimate stationary parameters, e.g. station positions, and to account for the highly variable stochastic behavior of non-stationary parameters like clocks or atmospheric delays. In this paper we investigate the accuracy of the Earth orientation parameters (EOP) estimated from the continuous VLBI campaign CONT14 using a Kalman filter. For comparison we calculated daily EOP using the least squares method (LSM) and stacked the resulting normal equations to gain continuous pole coordinates and dUT1 valued with a hourly resolution. For an external validation for polar motion we used hourly GPS estimates.
The CONT14 campaign: CONT14 was a campaign of continuous VLBI sessions, observed in May 2014, from the 6th at 00:00 UT through the 20th at 24:00 UT. CONT14 represents a continuation of the series of very successful continuous VLBI campaigns that have been observed at irregular intervals since 1994. The plan for the CONT14 campaign was to acquire state-of-the-art VLBI data over a time period of about two weeks to demonstrate the highest accuracy of which the current VLBI system is capable of. Seventeen globally distributed VLBI stations participated in this campaign. Figure 1 shows the geographic distribution.
Data analysis: The data from the CONT14 campaign were downloaded from the IVS website in NGS-format. The group delay ambiguities of these observations are already solved and the ionospheric delays calculated. The IERS Conventions 2010 were followed, and Vienna mapping functions (VMF1) used. Least squares method (LSM) • Hourly ZWD, gradients every 360 min • Clock as hourly piece-wise linear offsets • Radio source coordinates fixed to ICRF2 • NNR, NNT conditions applied • For hourly solution only x/y Pole and dUT1, but no nutation estimated
Fig. 1: The CONT14 station network
Noise modeling: One of the most crucial parts of the Kalman filter is the stochastic model, where the noise driving the parameter is introduced. For short temporal distances between the observations (
