Attitude Motion Characterization of Resident Space Objects via Fusion of Ground-based and Space-based Light Curves

Due to the growing number of fragmentationrelated debris and the launch of mega constellations of satellites, the characterization of Resident Space Objects has been assuming a growing importance in the context of Space Situational Awareness programs to enable accurate orbit propagations and related functionalities such as collision avoidance. Based on the analysis of light curves, photometric characterization can provide useful information concerning the objects’ surface material, shape, and attitude motion. In this context, this paper proposes an attitude motion classifier of unknown space objects using light curves. In particular, the focus is the combination of data from multiple sensors, either ground or space-based, in order to get a more reliable classification than the one arising from a single photometric measurement. Each light curve is classified using a spectral analysis method based on the Lomb-Scargle Periodogram and the Phase Dispersion Minimization approaches. The classifier’s outputs are then fused first at sensor level and then across multiple sensors to derive a unique classification for the observed space object. The performance of the presented architecture is assessed in a numerical environment able to reproduce synthetic light curves accounting for complex object geometries, and a realistic evolution of orbital and rotational dynamics. A correct classification has been produced for all the considered test cases preliminary proving the effectiveness of the proposed approach.