Shadow imaging of geosynchronous satellites: simulation, image reconstruction, and shadow prediction

Dennis M. Douglas; Bobby R. Hunt; David G. Sheppard

doi:10.1117/12.2238088

20 September 2016 Shadow imaging of geosynchronous satellites: simulation, image reconstruction, and shadow prediction

Dennis M. Douglas, Bobby R. Hunt, David G. Sheppard

Proceedings Volume 9982, Unconventional Imaging and Wavefront Sensing XII; 99820C (2016) https://doi.org/10.1117/12.2238088
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

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. Initial output from a shadow prediction tool indicates 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.

Conference Presentation

Citation Download Citation

Dennis M. Douglas, Bobby R. Hunt, and David G. Sheppard "Shadow imaging of geosynchronous satellites: simulation, image reconstruction, and shadow prediction", Proc. SPIE 9982, Unconventional Imaging and Wavefront Sensing XII, 99820C (20 September 2016); https://doi.org/10.1117/12.2238088

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