Abstract
Nanosatellite technology opens up new possibilities for earth observation. In the next decade, large satellite constellations will arise with hundreds, up to thousand of satellites in low earth orbit. A number of satellites will be equipped with rather low-cost sensors, such as GNSS receivers, suited for atmospheric monitoring. However, the future evolution in atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this regard, tomographic principles provide a unique opportunity for sensor fusion. The difficulty in performing the conversion of integral measurements into 3D images is that the signal ray path is not a straight line and the number of radio sources and detectors is limited with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative nonlinear. In this chapter, an overview about the individual solving techniques for the tomographic problem is presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and the L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.
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