EOP prediction using least square fitting and autoregressive filter over optimized data intervals

Xu, Xueqing; Zhou, Yonghong

doi:10.1016/j.asr.2015.08.007

Cited by 41 publications

(12 citation statements)

References 19 publications

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“…The UT1R-TAI time series obtained after preprocessing can all be divided into three parts: periodic, linear, and residual terms. The periodic and linear terms can be fitted and extrapolated by the LS model, and the residual terms can be fitted by the LS model and predicted by the AR model [14,28]. A brief principle of the combined LS + AR model and a new strategy for ultra-rapid UT1-UTC determination are introduced in the following.…”

Section: Methodsmentioning

confidence: 99%

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Yuan

Deng

2020

Remote Sensing

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Errors in ultra-rapid UT1-UTC primarily affect the overall rotation of spatial datum expressed by GNSS (Global Navigation Satellite System) satellite ultra-rapid orbit. In terms of existing errors of traditional strategy, e.g., piecewise linear functions, for ultra-rapid UT1-UTC determination, and the requirement to improve the accuracy and consistency of ultra-rapid UT1-UTC, the potential to improve the performance of ultra-rapid UT1-UTC determination based on an LS (Least Square) + AR (Autoregressive) combination model is explored. In this contribution, based on the LS+AR combination model and by making joint post-processing/rapid UT1-UTC observation data, we propose a new strategy for ultra-rapid UT1-UTC determination. The performance of the new strategy is subsequently evaluated using data provided by IGS (International GNSS Services), iGMAS (international GNSS Monitoring and Assessment System), and IERS (International Earth Rotation and Reference Systems Service). Compared to the traditional strategy, the numerical results over more than 1 month show that the new strategy improved ultra-rapid UT1-UTC determination by 29–43%. The new strategy can provide a reference for GNSS data processing to improve the performance of ultra-rapid products.

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Section: Methodsmentioning

confidence: 99%

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Yuan

Deng

2020

Remote Sensing

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“…The whole dataset are split up into two parts in such a way that the time-series from 1 January 1994 to 31 December 1997 are used to build the GM(1, 1) model with optimal sample number and the remaining part between 1 January 1998 and 10 December 1999 for evaluation of the established model. A comparison with the LS+AR and LS+ANN approaches which had been realized by Xu and Zhou (2015) and Schuh et al (2002) respectively is given in Figure 2 and Table 1, where the RMS error for the prediction intervals of 1ᝬ10 days is summarized, and 700 predictions starting at different days have been made for each prediction day to compute the RMS error, i.e. 700 l = .…”

Section: Prediction Results and Comparison With Other Methodsmentioning

confidence: 99%

“…A main conclusion of the EOP PCC is that there is not one particular prediction method superior to the others for all EOP components and all prediction intervals (Kalarus et al, 2010). The campaign has prompted researchers for more activity in this domain to improve the existing prediction approaches (Guo et al, 2013;Xu and Zhou, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Zhao

et al. 2016

Artificial Satellites

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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.

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“…These algorithms could be classified into two groups: first, the methods that use the information within the LOD time series, e.g., auto-covariance (AC) (Kosek et al 1998;Kosek 2002), wavelet decomposition (Akyilmaz et al 2011), or neural network (Schuh et al 2002;Liao et al 2012;Lei et al 2015Lei et al , 2017. Besides, this group includes the hybrid methods using the combination of least squares (LS) and auto-regressive (AR), auto-regressive moving average (ARMA), auto-covariance, and neural network (Kosek et al 2005;Xu and Zhou 2015;Wu et al 2019). In the second group, we cast the methods that take into account the axial component of effective angular momentum (EAM Z ) (Freedman et al 1994;Gross et al 1998;Niedzielski and Kosek 2008;Kosek 2012;Nastula et al 2012;Dill et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A new hybrid method to improve the ultra-short-term prediction of LOD

Modiri

Belda

Hoseini

et al. 2020

J Geod

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Accurate, short-term predictions of Earth orientation parameters (EOP) are needed for many real-time applications including precise tracking and navigation of interplanetary spacecraft, climate forecasting, and disaster prevention. Out of the EOP, the LOD (length of day), which represents the changes in the Earth's rotation rate, is the most challenging to predict since it is largely affected by the torques associated with changes in atmospheric circulation. In this study, the combination of Copula-based analysis and singular spectrum analysis (SSA) method is introduced to improve the accuracy of the forecasted LOD. The procedure operates as follows: First, we derive the dependence structure between LOD and the Z component of the effective angular momentum (EAM) arising from atmospheric, hydrologic, and oceanic origins (AAM + HAM + OAM). Based on the fitted theoretical Copula, we then simulate LOD from the Z component of EAM data. Next, the difference between LOD time series and its Copula-based estimation is modeled using SSA. Multiple sets of short-term LOD prediction have been done based on the IERS 05 C04 time series to assess the capability of our hybrid model. The results illustrate that the proposed method can efficiently predict LOD.

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EOP prediction using least square fitting and autoregressive filter over optimized data intervals

Cited by 41 publications

References 19 publications

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

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

A new hybrid method to improve the ultra-short-term prediction of LOD

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