Comparison of Novel Heuristic and Integer Programming Schedulers for the USAF Space Surveillance Network

Space is a highly congested and contested domain begetting the importance of prioritizing the Space Situational Awareness (SSA) mission. With increased dependence on space assets, scheduling and tasking of the Space Surveillance Network (SSN) is vitally important to maintaining space dominance. According to the 2004 USSTRATCOM Strategic Directive 505-1 (SD 505-1) the SSN uses centralized tasking, with decentralized scheduling. Enhancing SSA within available resources is paramount, and the development of a centralized SSN scheduler to maximize performance is crucial. This research develops and compares novel scheduling models to a model reflecting the 2004 SD 505-1. Novel schedulers were developed to reduce time gaps between observations, prioritize high value space objects, and retain maximum observation quality. In both single and multi-sensor scenarios, these novel schedulers maintained the same, or higher, levels of observation threshold retention in high priority targets, while increasing observation threshold gains in lower categories. Simulations using the novel schedulers showed dramatic improvement, especially in multi-sensor scenarios, in the mean and maximum time between observations of sample space objects compared to the SSN Scheduler Model. Novel schedulers showed at least a 3% improvement in meeting threshold requirements, a 12% decrease in mean time between observations, and up to a 9% decrease in maximum time between observations. Finally, these benefits were realized with a nominal increase in processing time for most novel schedulers. Results of this research can educate national policy makers on the benefits of proposed upgrades to current and future SSA systems