Artificial Intelligence and Machine Learning for Space Domain Awareness

To address the growing demands of operating in the space domain, space domain awareness (SDA) operators must determine how to prioritize sensor observations more effectively, scale up to meet the sheer volume of resident space objects, and develop analytic capabilities that reflect the complexity of orbital mechanics and space operations, all while maintaining the responsiveness necessitated by operations in a warfighting domain. These factors present significant challenges to those tasked with the SDA mission and point to this mission as a prime candidate for support from artificial intelligence (AI) and machine learning (ML) tools, because such tools have the potential to increase the analysis tempo, expand the amount of usable data for this analysis, and free up operator time for more-complex tasks. This report characterizes the nature of the impact that AI/ML tools could bring to the U.S. Space Force's SDA mission, with a focus on the conjunction assessment process to quantify the risk of collision in space. The impact of AI/ML tools has not been well understood, and this lack of understanding is a barrier to planning and optimizing the tools' integration. To support this assessment of AI/ML tools, the authors interviewed stakeholders, reviewed existing academic and doctrinal literature, developed detailed process maps, and built exploratory AI/ML models.