Shadow imaging of geostationary satellites

Overview & Synopsis

The submetric characterisation of objects in geostationary orbit is a key challenge for space surveillance, but it is a very ambitious objective at the limits of the resources currently being developed for astronomy. This work focuses on shadow imaging, a method that could potentially address this need through occultations. When a satellite passes in front of a star, it casts a shadow on the ground that encodes the geometric shape of the satellite. This shadow is then collected using small telescopes.To extract the information of interest, we need to digitally combine the measurements from the different telescopes by solving an inverse problem. The aim was first to study the light propagation model in order to validate the concept of the method and to specify and optimise the detector parameters. Then, the objective was to design the inversion algorithm and to estimate the performance of the overall imaging system.This work accurately quantified the atmospheric effects and found them to be relatively small. In addition, simple dimensioning rules were established based on a modal description of diffraction. A multispectral inversion algorithm was proposed and validated on realistic simulations, showing that sub-metric resolutions were indeed achievable. The suggested approach led to a significant simplification of the instrument and an improvement of the reconstructions. Finally, an experimental bench was developed to reproduce diffraction on a small scale under conditions equivalent to shadow imaging. This allowed the inversion to be validated on real data, thus confirming the technique's robustness.