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Improved Satellite Catalog Processing for Rapid Object Characterization
Phone: (410) 715-0005
Phone: (410) 715-0005
ABSTRACT: In this work we propose an integrated Finite Set Statistical (FISST) and hierarchical reasoning filtering solution to enable rapid catalog processing and object characterization for threat indication and warning. The solution relies on defining a hybrid discrete-continuous relational state between an asset and all potentially threatening space objects, and then applied to all assets. Given existing representative catalog, and raw sensor and other soft data, FISST allows for the extraction of the most amount of information contained in the data. This allows for a more accurate catalog processor leading to more rapid object assessment and characterization. Such accuracy and rapidity aids in faster and more accurate threat indications and warning. The proposed solution is probabilistically rigorous, scalable and can be implemented in real-time.; BENEFIT: This Phase I effort will be foundational to future work efforts by establishing a working prototype that exercises a variety of new probabilistically rigorous and scalable hybrid filtering techniques for threat indications and warning.It will begin with the divergence from the standard paradigm, where catalog processing is completely divorced from object characterization and vice-versa, into a functioning, scalable and rapid tool for threat indications and warning.This paradigm change is essential to evolve to higher levels of rapid object characterization and potentially orders of magnitude improvement in threat indication and warning.The prototype developed in this effort will demonstrate how to effectively implement the innovative FISST methodology in an ARCADE SOA environment.The inherent capabilities of FISST and hierarchical reasoning, in regards to probabilistic and confidence calculations, may then be exercised within the ARCADE and intelligence community counterparts to evaluate how they can influence operational commanders perspective on SSA.Finally, this work would demonstrate a viable set of SOA-available inference algorithms that are sensor and data type agnostic, real-time responsive, and scalable enough to support the massive new data rates of systems (e.g. Lockheed Space Fence) that the JMS will acquire in next few years.This project would set the ground work for a series of technologies that could significantly increase the capabilities of the JSpOC/JMS to help achieve their missions in SSA, Force Protection, and Combat Identification.
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