A statistical framework for space-based EUV ionospheric tomography

We present a statistical reconstruction framework for space-based extreme u ltraviolet (EUV) ionospheric tomography. The EUV technique offers a means t o invert the nighttime F region electron density on global scales from a si ngle spaceborne spectrograph, using prominent optically thin emissions prod uced by radiative recombination of O+. Since the EUV technique does not rel y on ground receivers to make the measurements, the observations do not suf fer from limitations on the viewing angles. The EUV tomography is an ill-co nditioned inverse problem in the sense that its solution is sensitive to pe rturbations of the measured data. With large condition numbers of a typical projection matrix, simple least squares inversion techniques yield unaccep table results in the presence of noise. This reflects the fact that more de grees of freedom are being sought than are supported by the noisy data. To overcome this limitation, we cast the tomographic inverse problem in a stoc hastic framework and incorporate a statistical prior model. In doing so we also obtain measures of estimation uncertainty for the solutions. Through s imulations, we demonstrate the applicability of these techniques in the con text of a space mission designed for EUV ionospheric tomography, namely, th e Tomographic Experiment Using Radiative Recombinative Ionospheric EUV and Radio Sources (TERRIERS). The simulations show promising results for EUV to mography as a viable ionospheric remote sensing technique.