Dissertation > Astronomy,Earth Sciences > Surveying and Mapping > Geodesy

Researches on the Theories and Algorithms of Earth Orientation Paramters Prediction

Author XuJunYi
Tutor YangYuanXi
School PLA Information Engineering University
Course Geodesy and Survey Engineering
Keywords Earth Orientation Parameters Earth's rotation Pole shift Length of day Robust Estimation Time Series Neural Networks Gray model
Type Master's thesis
Year 2010
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Affected by many of the excitation source , the instantaneous change of the orientation parameters of the earth is extremely complicated , detailed study of its variation for GEODYNAMICS has important significance . Meanwhile , satellite navigation , deep space exploration and other short-term changes in Earth Orientation Parameters practical applications require high precision value , and the value of the change is difficult to obtain real-time , hence the need for short - term forecasting of the Earth Orientation Parameters . The use of existing Earth Orientation Parameters time-series data in-depth study of the applicable short-term forecasting of the Earth Orientation Parameters , the main work is as follows: the brief polar motion and long cycle entry , excitation source , and the Earth Orientation Determination of parameters. Based on least squares fitting and forecasting analysis confirmed the robust estimation fit and prediction of Earth orientation parameters is more appropriate . 2, further study of the of ARMA model family in the Earth Orientation Parameters analysis found that the least squares prediction residuals , day length of the relevant characteristics of the AR model to describe the shift characteristics are closer to those ARIMA model . Thus, ARIMA models to forecast the pole shift parameters , and put forward a new intercept correction method to compensate for its prediction error . Explore the feasibility of direct neural network for short-term forecasts ; neural network function model , time series stochastic model , a combination prediction model , and compare with the neural network prediction model based on least squares do . Proposed gray model , time series , neural networks combined to predict the Earth Orientation Parameters . The results show that the gray model is simple and convenient , and to be able to take full advantage of the latest data modeling , and thus better dynamic real-time ; better than the commonly used prediction model based on a combination of gray model prediction accuracy combination forecasting model based on least squares .

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