A Kalman-filter-based approach to combining independent Earth-orientation series

Gross, R. S.; Eubanks, T. M.; Steppe, J. A.; Freedman, A. P.; Dickey, J. O.; Runge, Thomas F.

doi:10.1007/s001900050162

Cited by 74 publications

(47 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What can be said with the information available from the comparison is that the accuracy of the predictions for 1ᨺ5 days in the future by the GM(1, 1) model is equal to the prediction accuracy of the most accurate prediction techniques for UT1-UTC forecasts, namely Kalman filter (Gross et al, 1998) and adaptive transform from atmospheric angular momentum (AAM) to LODR , respectively. Since the 6 th day, the prediction error of the GM(1, 1) model gradually increases and is bigger than that of the two best techniques, but is noticeably lower than that of the other strategies yet.…”

Section: Prediction Results and Comparison With Other Methodsmentioning

confidence: 99%

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

Guo

Zhao

et al. 2016

Artificial Satellites

View full text Add to dashboard Cite

ABSTRACT.A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

show abstract

Section: Prediction Results and Comparison With Other Methodsmentioning

confidence: 99%

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

Guo

Zhao

et al. 2016

Artificial Satellites

View full text Add to dashboard Cite

show abstract

“…In Figure 6, we can see that the GPR can provide predictions that are equal to or better than those of other methods until the 300th day. After the 300th day, the GPR predictions are getting slightly worse than those of the number one (Gross et al (1998)). …”

Section: Y U L E I a N D O T H E R S Vol 68mentioning

confidence: 91%

“…A list of participants who supported the LOD predictions can be found in Kalarus et al (2010). What can be said with the information available from the comparison is that the accuracy of ultra short-term predictions by the GPR is inferior to the prediction accuracy of the number one (Gross et al (1998)) and the number two (Kalarus et al (2010)). For short-term predictions an accuracy is obtained which is inferior to the best presently available prediction method developed by Gross et al (1998).…”

Section: Y U L E I a N D O T H E R S Vol 68mentioning

confidence: 99%

Prediction of Length-of-day Using Gaussian Process Regression

Guo

Cai

et al. 2015

J. Navigation

View full text Add to dashboard Cite

The predictions of Length-Of-Day (LOD) are studied by means of Gaussian Process Regression (GPR). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. Firstly, well known effects that can be described by functional models, for example effects of the solid Earth and ocean tides or seasonal atmospheric variations, are removed a priori from the C04 time-series. Only the differences between the modelled and actual LOD, i.e. the irregular and quasi-periodic variations, are employed for training and prediction. Different input patterns are discussed and compared so as to optimise the GPR model. The optimal patterns have been found in terms of the prediction accuracy and efficiency, which conduct the multi-step ahead predictions utilising the formerly predicted values as inputs. Finally, the results of the predictions are analysed and compared with those obtained by other prediction methods. It is shown that the accuracy of the predictions are comparable with that of other prediction methods. The developed method is easy to use. K E Y WO R D S 1. Length-Of-Day (LOD).2. Prediction. 3. Gaussian Process Regression (GPR).

show abstract

“…The combination of UT1-UTC and LOD from VLBI and GPS is often thought to be extremely difficult, or at least not as straightforward as for the pole coordinates (e.g., , Gross et al 1998. Since satellite orbits have to be estimated in global GPS solutions, the orientation of the Earth in space given by the nutation angles and the diurnal rotation UT1-UTC is not accessible in an absolute sense.…”

Section: Universal Timementioning

confidence: 99%

“…This means that not only PM coordinates including their time derivatives were combined, but also the UT1-UTC time-series and the nutation angles results from combining the contributions of GPS and VLBI. Although the IERS is approaching a simultaneous combination of the TRF together with the EOPs, the inclusion of GPS into time-series of UT1-UTC is thought to be problematic , Gross et al 1998, Gross 2000 and the officially available GPS solutions do not even contain the nutation angles. Thus, up to now, a consistent combination has been performed only by Yaya (2002), where all space-geodetic techniques were analyzed with the same software package and the resulting normal equations were subsequently combined, and by Andersen (2000), where all observations were analyzed within one step.…”

Section: Introductionmentioning

confidence: 99%

Combined Earth orientation parameters based on homogeneous and continuous VLBI and GPS data

Thaller

Krügel²,

Rothacher

et al. 2006

J Geod

View full text Add to dashboard Cite

Abstract. The CONT02 campaign is of great interest for studies combining very long baseline interferometry (VLBI) with other space-geodetic techniques, because of the continuously available VLBI observations over two weeks in October 2002 from a homogeneous network. Especially the combination with the Global Positioning System (GPS) offers a broad spectrum of common parameters. We combined station coordinates, Earth orientation parameters (EOPs) and troposphere parameters consistently in one solution using technique-specific datum-free normal equation systems. In this paper, we focus on the analyses concerning the EOPs, whereas the comparison and combination of the troposphere parameters and station coordinates is covered in a companion paper in Journal of Geodesy. In order to demonstrate the potential of the VLBI and GPS space-geodetic techniques, we chose a sub-daily resolution for polar motion (PM) and universal time (UT). A consequence of this solution set-up is the presence of a one-to-one correlation between the nutation angles and a retrograde diurnal signal in PM. The Bernese GPS Software used for the combination provides a constraining approach to handle this singularity. Simulation studies involving both nutation offsets and rates helped to get a deeper understanding of this singularity. With a rigorous combination of UT1-UTC and length of day (LOD) from VLBI and GPS, we showed that such a combination works very well and does not suffer from the systematic effects present in the GPS-derived LOD values. By means of wavelet analyses and the formal errors of the estimates, we explain this important result. The same holds for the combination of nutation offsets and rates. The local geodetic ties between GPS and VLBI antennas play an essential role within the inter-technique combination. Several studies already revealed non-negligible discrepancies between the terrestrial measurements and the space-geodetic solutions. We demonstrate to what extent these discrepancies propagate into the combined EOP solution.

show abstract

A Kalman-filter-based approach to combining independent Earth-orientation series

Cited by 74 publications

References 46 publications

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

Prediction of Length-of-day Using Gaussian Process Regression

Combined Earth orientation parameters based on homogeneous and continuous VLBI and GPS data

Contact Info

Product

Resources

About