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Estimation of the ionospheric disturbances behavior is very important in many applications, including efficient Synthetic Aperture Radar (SAR) signal processing through accurate noise modeling and removal, monitoring of environmental evolution and geodynamics SAR Interferometry (InSAR) purposes. Modeling ionospheric disturbances behavior is a challenging research issue which involves many non-linearities, dynamics and external factors. In this paper, we propose a dynamic, recursive highly non-linear forecasting model regarding the ionospheric component of the spaceborne InSAR technique. In particular, we introduce a framework which takes into consideration the error between the predicted and the actual data and in the sequel adapt a highly non-linear model in a way to optimize prediction accuracy. In this way, we face the problems arising from the traditional approaches which try to pre-compute the noisy effects of the wave propagation through ionosphere on spaceborne SAR images. The model exploits concepts from functional analysis and represents an unknown non-linear function using a series of known functional components, which are then used for ionospheric forecasting. Emphasis will be given in the computational complexity of the model so that the forecasting will be accomplished in real time context which can be applied in Dynamic Synthetic Aperture Radar Interferometry (DInSAR) technique. The model has been tested with ionospheric noise derived from real interferograms produced by earthquakes occurred in Greece the last fifteen years. Specifically, using this adaptive non-linear modeling we extract the noise due to the ionospheric propagation from comparatively processed interferograms, gathered from different highly seismicity areas in Greece. Additionally, we compare the observed Total Electron Content (TEC) during ionospheric disturbances, using the ionospheric station at the National Observatory of Athens and the main Global Position System (GPS) Station at Dionysos Satellite Observatory (DSO) of the National Technical University of Athens (NTUA), Greece, with the ionospheric TEC derived from the non-linear adaptive modeling. |
