Heimdall MSSS Strategic Collection

ABSTRACT: Orbit Logic and the University of Colorado propose the Heimdall System to schedule observations of known objects and search for and establish custody of unknown objects from the Maui Space Surveillance Site. The Heimdall System includes a modeling environment a scheduling engine, and a track prioritization component. Within the Heimdall System, Finite Set Statistics (FISST) methods will be used to prioritize observations of known objects and identify search areas for unknown objects that are then planned with specialized scheduling algorithms and configurable SSA-specific figure-of-merit scoring. The proposed Phase II effort will build upon the successful Heimdall System prototype which demonstrated track prioritization, system modeling, an SSA-specific figure-of-merit, and automated planning algorithms. Phase II will focus on Heimdall System optimization improvements and the integration of Heimdall System capabilities into MSSS planning operations.; BENEFIT: The Heimdall software for SSA sensor tasking will provide greatly improved performance over manual tasking, improved coordinated sensor usage, and tasking schedules driven by accurate system modeling and catalog improvement goals (reduced overall covariance, etc.). The improved scheduling performance also enables more responsive sensor tasking to address external events, newly detected objects, newly detected object activity, and sensor anomalies. Instead of having to wait until the next routine scheduling phase, events can be automatically addressed with new tasking schedules in near-real-time (within seconds or minutes). Perhaps the most important benefit is improved SSA based on an overall improvement to the quality of the space catalog. By driving sensor tasking and scheduling based on better system modeling, predicted information gain and other relevant factors, better decisions are made in the application of available sensor resources, leading to more appropriate observation schedules, an improved object catalog and better information about the objects of greatest interest. Another benefit of the proposed Phase II innovation is a new self-tuning Figure-of-Merit technology that will auto-adjust FOM weighting factors based on high-level goals (such as reduced overall catalog covariance and/or shorter lag time between the detection of a new object and follow-on observations).