Modeling the problem of low-orbital satellite UV-tomography of the ionosphere

The results of modeling the direct and inverse problems of low-orbital satellite ultraviolet (UV) tomography of the ionospheric 135.6 OI volume emission rate are presented. The direct problem was solved with the orbital geometry of DMSP block 5D3 satellites with SSUSI and SSULI UV spectrographs among the other payloads, the real operating parameters of these instruments (the scan rate and the interval of scan angles), and the set of the model distributions of the volume emission rate that contain irregularities on various scales. The solution of the direct problem yields the radiation intensities in the 135.6 nm line, which is used as the input data for reconstructing the initial (prototype) model distributions of the volume emission rates. The obtained system of linear equations (SLE) was solved using the Algebraic Reconstruction Technique (ART) and Simultaneous Iterative Reconstructive Technique (SIRT) algorithms, which are highly efficient in problems of the low-orbit radio tomography of the ionosphere. It is shown that the initial model distribution can be successively reconstructed if one takes the non-negativity condition of the solution into account, uses weighting functions to decrease the solution in the regions where it is known to be a priori small, and applies inter-iteration smoothing to eliminate the effects of the approximation errors. Here, the averaging parameters should decrease in the course of the iterations. With these constraints fulfilled, the computational costs of the ART- and SIRT-based solutions are similar, while the reconstruction error is approximately 6\%. The influence of random errors and bias in the data on the results of the reconstruction is explored. It is shown that with a given error level of the initial data the parameters of the reconstruction algorithms can be adjusted in such a way as to efficiently suppress the influence of the noise with a relative amplitude of 2\textendash3\% on the solution.