Tekil Değer Ayrışımı ile 2-B Toplam Elektron İçeriği Yeniden Yapılandırılması

Abstract

Ionosphere affects the performance of shortwave communication, satellite communications, space-based navigation and positioning systems. To enhance the performance of these systems, understanding the structure of the Ionosphere is crucial. The Ionosphere has inhomogeneous, anisotropic, space and time varying, spatio-temporal dispersive behaviour. Total Electron Content (TEC) is an important parameter of Ionosphere for understanding the structure of ionosphere. TEC can be estimated at limited number of points in space. Therefore, there is a need for accurate, reliable, and robust TEC mapping methods. In this study, Singular Value Decomposition (SVD) and Least Square methods are employed to perform 2-D reconstruction of the European mid-latitude Ionosphere. The SVD provides an expansion onto physical basis vectors. The output contains the minimum basis vector with maximum energy. In this study, the 'Model Matrices',on which the SVD is applied, are generated based on the 11-yearly and monthly cycles of the Sun and the geomagnetic indices of the Earth. The structure of these model matrices is one of the factors contributing to the high performance of the basis vectors obtained through SVD in ionospheric reconstruction. The Least Squares is used for 2-D TEC reconstruction by measurement TEC values and the basis vectors obtained through SVD. 99.9952% of there constructed 145,152 TEC values has less than 3 TECU difference with JPL-TECs. Probability Density Function (PDF) is estimated for this TEC differences, between JPL-TEC and the reconstructed maps. The TEC difference pdfs are shown to be Laplace Distributed. The mean of this distribution indicates no bias in TEC reconstructions. The developed reconstruction algorithm and proposed application can compute the TEC maps in closed form, without any computational complexity. High performance can be achieved by using small number of basis vectors in signal subspace. All of these features make the algorithm reliable, accurate, and robust. The algorithm developed in this thesis can be applied to all Ionosphere states for regional and global TEC mapping. In application, the signal subspace can be determined based on the estimated PDF parameters obtained in this thesis. This reconstruction algorithm can be used for near-real time estimation and near-real time prediction of TEC maps.