This report characterizes the nature of the impact that artificial intelligence and machine learning (AI/ML) tools could bring to the U.S. Space Force's space domain awareness (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.

Artificial Intelligence and Machine Learning for Space Domain Awareness

Characterizing the Impact on Mission Effectiveness

Li Ang Zhang, Krista Langeland, Jonathan Tran, Jordan Logue, Prateek Puri, George Nacouzi, Anthony Jacques, Gary J. Briggs

ResearchPublished Nov 21, 2024

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.

Key Findings

  • Because of the growing demands and changing nature of the SDA mission, AI/ML tools have a high opportunity for impact if they can be force multipliers for SDA operators.
  • AI/ML tools cannot address all the challenges of the SDA mission, but process changes can help these tools achieve greater impact.
  • Realizing significant impacts from AI/ML requires moving to an architecture that enables more AI/ML development and fielding.
  • AI/ML tool development that supports more-optimized sensor tasking could have a cascading impact on the rest of the SDA mission.
  • Better quantification of risk and uncertainty tolerance can support the improved performance of AI/ML tools focused on prediction and classification.

Recommendations

  • Space Systems Command Acquisition Delta-SDA (SSC/SZG) and the Air Force Research Laboratory should provide clear guidance to AI/ML tool developers to focus on tools that address the needs of operators today but could also benefit a future SDA architecture.
  • AI/ML tool development should focus on force multipliers that help meet the growing detection and characterization challenges faced by the 18th and 19th Space Defense Squadrons. SSC/SZG should look for opportunities to invest in these tools.
  • SDA operators — in particular, at the 18th and 19th Space Defense Squadrons and the National Space Defense Center — should seek ways to articulate mission needs, sensor requirements, and acceptable uncertainty to optimize potential enhancements from AI/ML tools.
  • Space Operations Command, via the SDA Mission Area Team, should examine where SDA processes could be modified to enable more-significant impact from AI/ML tools.
  • AI/ML tool development should focus on those tools that enable more-effective and more-efficient sensor tasking, and SSC/SZG should seek ways to acquire and develop those tools. Efforts at the 18th Space Defense Squadron to capture observation intent should also be supported, as these efforts, in combination, are key enablers.
  • SSC/SZG should continue to support efforts in AI/ML tool development and ensure that there are processes and infrastructure in place to test and validate these models.
  • The availability of high-quality training data is another enabler for AI/ML impact, and the Space Force should support efforts at Space Systems Command in its Cross Mission Data team to ensure the availability of these data to AI/ML tool developers.

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Document Details

  • Copyright: RAND Corporation
  • Availability: Available
  • Year: 2024
  • Print Format: Paperback
  • Paperback Pages: 62
  • Paperback Price: $33.00
  • Paperback ISBN/EAN: 1-9774-1447-8
  • DOI: https://doi.org/10.7249/RRA2318-1
  • Document Number: RR-A2318-1

Citation

RAND Style Manual

Zhang, Li Ang, Krista Langeland, Jonathan Tran, Jordan Logue, Prateek Puri, George Nacouzi, Anthony Jacques, and Gary J. Briggs, Artificial Intelligence and Machine Learning for Space Domain Awareness: Characterizing the Impact on Mission Effectiveness, RAND Corporation, RR-A2318-1, 2024. As of April 30, 2025: https://www.rand.org/pubs/research_reports/RRA2318-1.html

Chicago Manual of Style

Zhang, Li Ang, Krista Langeland, Jonathan Tran, Jordan Logue, Prateek Puri, George Nacouzi, Anthony Jacques, and Gary J. Briggs, Artificial Intelligence and Machine Learning for Space Domain Awareness: Characterizing the Impact on Mission Effectiveness. Santa Monica, CA: RAND Corporation, 2024. https://www.rand.org/pubs/research_reports/RRA2318-1.html. Also available in print form.
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The research reported here was commissioned by the Chief Scientist of the U.S. Air Force (AF/ST) and conducted within the Force Modernization and Employment Program of RAND Project AIR FORCE.

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