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Author: Wu, Devin × End date: 2020 × Start date: 2020 × search.filters.applied.f.isOrgUnitOfPublicationTitle: Daniel Guggenheim School of Aerospace Engineering ×

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SDA Visible Range Object Detection Performance Optimization Under Urban Environment

2020-05-01 , Wu, Devin

The ability to track and detect Resident Space Objects (RSO) is essential for maintaining a comprehensive Space Domain Awareness (SDA), especially with the exponential increase in popularity and population of the smaller format satellites. The Georgia Tech Space Object Research Telescope (GT-SORT) maintained by the Daniel Guggenheim School of Aerospace Engineering is one of the unique university-owned raven class telescope facilities dedicated to SDA research tasks. Located on the Georgia Tech campus near downtown Atlanta, the GT SORT observation site has the advantage of high accessibility for researchers and students alike; however, it suffers from artificial light pollution more so than other traditional observatories. A performance optimization on RSO detection of the facility is therefore critical to extrapolate its full potential. Previous effort has shown that observations of common RSO material, such as gold, white paint, and various types of solar panels, yields the highest limiting magnitude in the near infrared (NIR) and the short wave infrared (SWIR) region [1]. However, the widely used CMOS based camera sensor has a relative quantum efficiency characteristic that drops below 10% at 950nm and above, and the SWIR cameras with mega pixel or higher resolution capability are enlisted under ITAR restricted technology, which restricts the availability of such sensor type data for the general public and for fundamental research purpose. Therefore, spectral optimization within the CMOS camera capability on common RSO surface material types are performed in this study along with collected spectral satellite data to support the optimized model

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SDA Visible Range Object Detection Performance Optimization Under Urban Environment

2020-05 , Wu, Devin

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