This paper introduces the reader to our Kalman filter developed for geodetic VLBI（very long baseline interferometry） data analysis. The focus lies on the EOP（Earth Orientation Parameter） determination based on the...This paper introduces the reader to our Kalman filter developed for geodetic VLBI（very long baseline interferometry） data analysis. The focus lies on the EOP（Earth Orientation Parameter） determination based on the Continuous VLBI Campaign 2014（CONT14） data, but also earlier CONT campaigns are analyzed. For validation and comparison purposes we use EOP determined with the classical LSM（least squares method） estimated from the same VLBI data set as the Kalman solution with a daily resolution. To gain higher resolved EOP from LSM we run solutions which yield hourly estimates for polar motion and dUTl = Universal Time（UT1）-Coordinated Universal Time（UTC）. As an external validation data set we use a GPS（Global Positioning System） solution providing hourly polar motion results.Further, we describe our approach for determining the noise driving the Kalman filter. It has to be chosen carefully, since it can lead to a significant degradation of the results. We illustrate this issue in context with the de-correlation of polar motion and nutation.Finally, we find that the agreement with respect to GPS can be improved by up to 50% using our filter compared to the LSM approach, reaching a similar precision than the GPS solution. Especially the power of erroneous high-frequency signals can be reduced dramatically, opening up new possibilities for highfrequency EOP studies and investigations of the models involved in VLBI data analysis.We prove that the Kalman filter is more than on par with the classical least squares method and that it is a valuable alternative, especially on the advent of the VLBI2010 Global Observing System and within the GGOS frame work.展开更多
基金supported by the Austrian Science Fund(FWF),project P24187-N21
文摘This paper introduces the reader to our Kalman filter developed for geodetic VLBI（very long baseline interferometry） data analysis. The focus lies on the EOP（Earth Orientation Parameter） determination based on the Continuous VLBI Campaign 2014（CONT14） data, but also earlier CONT campaigns are analyzed. For validation and comparison purposes we use EOP determined with the classical LSM（least squares method） estimated from the same VLBI data set as the Kalman solution with a daily resolution. To gain higher resolved EOP from LSM we run solutions which yield hourly estimates for polar motion and dUTl = Universal Time（UT1）-Coordinated Universal Time（UTC）. As an external validation data set we use a GPS（Global Positioning System） solution providing hourly polar motion results.Further, we describe our approach for determining the noise driving the Kalman filter. It has to be chosen carefully, since it can lead to a significant degradation of the results. We illustrate this issue in context with the de-correlation of polar motion and nutation.Finally, we find that the agreement with respect to GPS can be improved by up to 50% using our filter compared to the LSM approach, reaching a similar precision than the GPS solution. Especially the power of erroneous high-frequency signals can be reduced dramatically, opening up new possibilities for highfrequency EOP studies and investigations of the models involved in VLBI data analysis.We prove that the Kalman filter is more than on par with the classical least squares method and that it is a valuable alternative, especially on the advent of the VLBI2010 Global Observing System and within the GGOS frame work.
