Shadow imaging is a technique to obtain highly resolved silhouettes of resident space objects (RSOs) which would otherwise be unattainable using conventional terrestrial based imaging approaches. This is done by post processing the measured irradiance pattern (shadow) cast onto the Earth as the RSO occults a star. The research presented here focuses on shadow imaging of geosynchronous (GEO) satellites with near stationary orbits approximately 36,000 km from the Earth. Shadows pertaining to a set of diverse observing scenarios are simulated and used as inputs to a Fresnel based phase retrieval algorithm. Spatial resolution limits are evaluated and correlated to signal to noise (SNR) metrics. Resolvable feature sizes of less than 1 m are shown to be readily achievable using foreseeable observing scenarios. The development of a shadow prediction capability is outlined with initial output indicating that there are, on average, over 1000 shadows on the Earth on any given time from a single GEO satellite for stars brighter than m_v=10. Shadow ground track uncertainties are correlated to stellar astrometric errors. Global and localized shadow track maps are presented demonstrating a high feasibility for future shadow collections.
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