Accurate and rapid space object behavioral tracking enables space protection and space domain awareness (SDA). Recent methods of artificial intelligence and machine learning (AI/ML) enhance space object behavior classification of evasive satellite behaviors detection within the Adaptive Markov Inference Game Optimization (AMIGO) tool. AMIGO integrates data fusion, stochastic modeling and, and AI/ML pattern classification. Numerical simulations demonstrate the advantage of using the Uncertainty Representation and Reasoning Evaluation Framework (URREF) for space ontological pattern of life assessment of veracity, precision, and recall when a resident space objects conducts a maneuver.
Space Object Tracking Uncertainty Analysis with the URREF Ontology
06.03.2021
5046664 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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