Authors
Nacouzi, George, Jacques, Anthony, Zhang, Li Ang, Langeland, Krista, Tran, Jonathan, Logue, Jordan, Briggs, Gary J., Puri, Prateek
Related Organizations
- Division
- RAND Project AIR FORCE Force Modernization and Employment Program
- Pages
- 72
- Published in
- United States
- RAND Identifier
- RR-A2318-2
- RAND Type
- report
- Rights
- RAND Corporation
- Series
- Research Reports
- Source
- https://www.rand.org/pubs/research_reports/RRA2318-2.html
Table of Contents
- Artificial Intelligence and Machine Learning for Space Domain Awareness 1
- About This Report 3
- RAND Project AIR FORCE 3
- Acknowledgments 4
- Summary 5
- Issue 5
- Approach 5
- Key Findings 6
- Recommendations 6
- Contents 7
- Figures and Tables 8
- Figures 8
- Tables 9
- Space Domain Awareness Mission Overview 11
- Space Command and Control 12
- CA Process 21
- Future Data Architecture Uncertainties 25
- Artificial Intelligence and Machine Learning Overview 27
- Current AIML Efforts in SDA 27
- Selection of Two Case Studies 29
- Neural Network Architecture 30
- Managing Uncertainty 32
- Model Evaluation 32
- Summary 34
- Elliptical Screening Process Tool 35
- Elliptical Screening Workflow Description 35
- Data Generation 36
- Model Architecture 37
- Model Output and Postprocessing 41
- Implementation Challenges and Future Work 44
- Summary 45
- Orbital State Propagator Tool 46
- Covariance Propagation Workflow Description 46
- Data Generation 47
- Model Architecture 50
- Model Performance 52
- Outputs and Aggregation 52
- Ground truth comparison 52
- Additional BNN Features 55
- Operational Frameworks 59
- Implementation Challenges and Future Work 61
- Summary 62
- Conclusion 63
- Key Findings 63
- Recommendations 64
- Abbreviations 65
- References 67
