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.


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    Title :

    Space Object Tracking Uncertainty Analysis with the URREF Ontology


    Contributors:
    Blasch, Erik (author) / Shen, Dan (author) / Chen, Genshe (author) / Sheaff, Carolyn (author) / Pham, Khanh (author)


    Publication date :

    2021-03-06


    Size :

    5046664 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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