Estimating dynamic ionospheric changes without a priori models

We present a novel artificial-neural-network-based computerized ionospheric tomography technique, capable of imaging through time in three-dimensional space. Total electron content (TEC) data collected from a satellite passin g over the region of interest are used to train a neural network. The train ed network creates estimates of the difference in ionospheric electron dens ity between time steps based on the TEC data. Application of the difference estimate at each time step to the previous electron density image results in a time-varying ionospheric electron density estimate. Experimental resul ts on synthetic data are presented that demonstrate that the algorithm is c apable of detecting short-term localized disturbances in the ionosphere.