3-D Computerized ionospheric tomography with random field priors

Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded GPS signals. Due to the multi-scale variability of the ionosphere and inherent biases and errors in the computation of TEC, CIT constitutes an underdetermined ill-posed inverse problem. In this study, CIT is performed by using a Bayesian approach with Gaussian random field priors. The 3-D mean and the covariance of the assumed Gaussian random field priors can either be obtained from ionospheric models such as IRI or they can be estimated by an iterative algorithm from the GPS measurements. Given sparse and non-uniform TEC measurements, the electron field is obtained from mean square estimation where the Gaussian random field structure provides regularization. Geographical and temporal variations of ionosphere can be observed by obtaining tomographic reconstructions of electron density distribution from Earth-based GPS stations for both quiet and disturbed days of ionosphere. 2-D slices that will be obtained from 3-D reconstructions can be compared with the model based reconstructions or with the available Global Ionospheric Maps from IGS centers.