Improved Satellite Catalog Processing for Rapid Object Characterization

To further enhance our ability to assess orbital events in the context of highly complex and ever evolving situations, provide a fuller understanding of the space operational picture, support indications and warnings, and to provide tactical protection of U.S. space assets, ADS has assembled a world class team of experts in the fields of Finite Set Statistics (FISST) and probabilistic reasoning for resident space object (RSO) recognition and identification. JMS requires a scalable cataloging architecture that accommodates high volumes of real-time raw measurement data processing and enable the JSpOC to appropriately balance their surveillance resources between catalog maintenance and tactical monitoring. Space event analysis is typically treated as the detection, tracking, identification, and characterization of individual RSOs where we seek to assess and analyze their interactions and patterns of behavior to ascertain potential threats. The ADS team presents an innovative information theoretic filtering methodology that allows the detection, correlation and threat characterization of pair-wise asset-RSO space threats. In this work, we apply powerful and intuitive hierarchy constructs to jointly reason between threat considerations, object characteristics, and mission specific behaviors. To this end, ADS proposes the incremental design and development of a scalable, automated rapid cataloging and characterization system called Semnai.