Monitoring of the Earth’s ionosphere by a small satellite constellation

Existing radar aids for the research of the ionosphere and ionosondes allow only local diagnostics of the ionosphere. Creating a network of traditional tools of radio sounding of ionosphere is quite difficult and expensive. Using constellations of LEO satellites in ionospheric radio-tomography probl...

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Bibliographic Details
Main Authors: O. V. Phylonin, P. N. Nikolayev
Format: Article
Language:English
Published: Samara National Research University 2016-04-01
Series:Вестник Самарского университета: Аэрокосмическая техника, технологии и машиностроение
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Online Access:https://journals.ssau.ru/vestnik/article/viewFile/3028/2979
Description
Summary:Existing radar aids for the research of the ionosphere and ionosondes allow only local diagnostics of the ionosphere. Creating a network of traditional tools of radio sounding of ionosphere is quite difficult and expensive. Using constellations of LEO satellites in ionospheric radio-tomography problems is today a promising direction of ionospheric research that allows getting the latest update about its condition. It is necessary to develop new, highly efficient methods and tools for solving problems of this class. An approach for the reconstruction of vertical distribution of ionospheric electron density by processing satellite-to-satellite signals is discussed in the article. The form of a promising orbital constellation of small satellites that makes it possible to obtain the cross section of the ionospheric electron density in the plane of the orbit is determined. It was found that the problem of 2D reconstruction of electron density vertical distribution in the ring layer of the ionosphere can be solved during the orbital period of the satellite in the constellation, the number of satellites in which is 5 to 7, depending on the height of the orbit. It is shown that as the number of vehicles in the constellation increases up to the amount that fills the orbit, the reconstruction time could be reduced to 10 minutes. It is shown that the problem is reduced to a few-view tomography problem using the method of convolutional filtering.
ISSN:2542-0453
2541-7533