Intelligent Space Surveillance Network (SSN) Scheduling Applications Using Artificial Intelligence Techniques

ABSTRACT: The ultimate goal of this effort is to improve the performance of the Space Surveillance Network (SSN) including improved space catalog accuracy through more and more complementary tracking, better responsiveness to real-time changes, better tracking of small debris in LEO through efficient use of applicable sensors, efficient tracking of DS frequent revisit objects, handling increased number of objects and new types of sensors, and taking advantage of improved communication and control to globally optimize the SSN schedule. The next logical step is to develop the full-scale family of SSN schedulers in our current Aurora framework and integrate them with the appropriate SSN systems. These include the GIG-enabled Tasker Optimizing Scheduler and the scheduling portions of the Next Generation Tasker (NGT), including a LEO frequent revisit optimizing scheduler, a DS frequent revisit optimizing scheduler, and a Global Master Allocator Optimizing Scheduler. This will require early, frequent, and long-lasting contact with the Next Generation Tasker (NGT) initiative led by the Air Force Space Command (AFSPC/A9A, letter of support from Colonel Visel included) to ensure full understanding of the requirements, constraints, opportunities, and integration issues. We also will provide advice and support to the NGT initiative from a resource optimization and scheduling perspective. BENEFIT: The most direct target for the results of this effort is, of course, the SSN itself including both the Next Generation Tasker (NGT) and the GIG-Enabled Tasker. Attached is a letter of support from Colonel Visel which says, in part,"Our interactions have convinced us that there are several areas in our NGT effort that could benefit from Stottler Henke"s expertise. Specifically, we see applicability for their Aurora tool to solve constrained scheduling problems in both frequent-revisit and catalog-maintenance sensor tasking."By both demonstrating significantly improved performance during Phase II and ensuring that the ultimate results can be integrated into the operational scheduling systems, the Air Force will have a strong incentive to operationalize the Phase II development of the proposed Schedulers. Beyond the Air Force there are several commercialization possibilities available in the government and private sector. The algorithms developed here could be marketed to our existing Army and Navy sensor customers interested in sensor scheduling and coordination. Finally, there are the commercial applications for the technology developed for the Scheduler. We are already marketing Aurora, our general intelligent planning and scheduling framework, and customizing it for a variety of domains. The additional capabilities developed for SSN scheduling can be synergistically marketed to the same clients. For example, the bottleneck avoidance algorithm proposed to be fully developed here would be applicable to many Aurora customers. We anticipate that this effort will result in additional scheduling algorithms that we will be able to incorporate into our existing scheduling products, thus increasing the benefits they provide and their value.