Exploration of Compressed Sensing for Satellite Characterization

This research introduces a satellite characterization method based on its light curve by utilizing and adapting the methodology of compressed sensing. Compressed sensing is a mathematical theory, which is established in signal compression and which has recently been applied to an image reconstruction by single-pixel camera observation. In this thesis, compressed sensing in the use of single-pixel camera observations is compared with a satellite characterization via non-resolved light curves. The assumptions, limitations, and significant differences in utilizing compressed sensing for satellite characterization are discussed in detail. Assuming a reference observation can be used to estimate the so-called sensing matrix, compressed sensing enables to approximately reconstruct resolved satellite images revealing details about the specific satellite that has been observed based solely on non-resolved light curves. This has been shown explicitly in simulations. This result implies the great potential of compressed sensing in characterizing space objects that are so far away that traditional resolved imaging is not possible.