Research On Forecasting And Application Of The Ionospheric Total Electron Content Based On GNSS

As the GNSS (Global Navigation Satellite System) has been more and more widely used in various fields, the research on ionosphere is developing by leaps and bounds and becoming an inter-discipline topic. Research concerning ionosphere based on GNSS dada, especially on ionospheric total electron content (TEC) based on GPS data, not only deepens the understanding of the structure and change rule of the ionosphere, but also provides substantial support for practical applications such as the model foundation for ionospheric delay correction and the improvement of the positioning accuracy for the navigation and positioning system of the satellite.In this dissertation, the analysis, modeling, prediction, application and other issues focusing on ionosphere TEC data has been deeply discussed. The characteristics of spatial and temporal variations of TEC and the correlation between solar activity and TEC are analyzed, and the rule for the long-term variation of the ionospheric TEC is modeled and predicted. In order to reduce the complexity of the model, the time series analysis model is used to model and forecast the short-term variation of ionospheric TEC, and the model is applied to detect the TEC anomaly prior to Wenchuan earthquake. The specific work of the dissertation is as follows:Firstly, in order to quantitatively describe the characteristics of the long-term ionospheric TEC, the empirical model is proposed based on sinusoidal function modulation and used to forecast monthly median TEC. The daily and monthly variations of the ionospheric TEC in different solar activity years and the spatial and temporal variations of the relative change rate of the TEC difference are studied through analyzing and processing the TEC data from five tracking stations in China. Modeling and prediction of monthly median TEC data from five tracking stations and global areas with different latitudes are realized through combining the long-term trend component characterized by sinusoidal function with the cyclic component characterized by harmonic function. The modeling results have been verified by the IGS data, and the verification result is satisfying, which also provides necessary support for further research on the correlation between solar activity and the ionospheric TEC.Secondly, to quantitatively describe the influence of the solar activity on the spatial and temporal variations of the ionospheric TEC, the empirical model used to forecast the monthly median TEC is proposed on base of perturbation factors modulated by linear function. Based on the long-term observation data, the correlation between the sunspot number and the global monthly ionospheric TEC at different latitudes is analyzed. Using the linear variation trend of the above mentioned correlation to modulate the perturbation factors and taking the sunspot number as the input parameter, the global monthly median TEC at different latitudes are modeled and predicted. Then the modeling results are verified by the IGS data. Thus the long-termed dependence of the monthly ionospheric TEC on the solar activity is quantitatively described, which provides a basis for effective improvement of the ionospheric TEC prediction model.Thirdly, in order to optimize the modeling process of the ionospheric TEC, and to improve the prediction accuracy of the hourly ionospheric TEC, the direct prediction method for the ionospheric TEC based on the autoregressive moving average (ARMA) model with residual correction of prediction is proposed. The advantage of the method is that it avoids the overmuch discussion of the complex characteristics of the ionospheric TEC. The method reveals the structure and law of the dynamic data itself and the correlation between the observation data through the statistical analysis based on the observation data and thus realize the prediction of the ionospheric TEC data, and improve the prediction precision of the short-term TEC. The actual effect of this method for forecasting the hourly ionospheric TEC has been verified on base of the IGS data, which provides necessary reference for the optimization of the ionospheric TEC model.Finally, in order to improve the detection accuracy of ionospheric TEC anomaly prior to the earthquake, a new method for detecting TEC anomaly is proposed. The method can detect the abnormal TEC disturbance prior to the earthquake through setting a reasonable decision threshold of tolerance and applying the TEC values predicted by ARMA model with the residual correction instead of the values obtained by traditional methods. The data analysis of the ionosphere TEC before and after the Wenchuan earthquake shows the advantage of our method compared to other ones, that is, the method can be used to discover not only the abnormal TEC data detected by other methods, but also some new TEC anomaly at different time instants prior to the earthquake.Based on the above theoretical research and project design, all the work has been experimentally verified using the ionospheric TEC data provided by IGS group and gives results as what is expected, which indicates that the research results would provide important scientific significance and application value for deepening the theoretical study of the ionosphere TEC and improving the influence of the ionospheric delay on the global navigation satellite system.This work was supported by National High Technology Research and Development Program 863 (The Research on New Technology of improving the Performance of GNSS Receiver by using Multipath Signals, No.2009AA12Z312), and the National Natural Science Foundation of China (The Research on the Theory and Algorithm of improving GNSS Receiver Capture Sensitivity Based on Rake-CL, No.60972091).