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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    Titel :

    Space Object Tracking Uncertainty Analysis with the URREF Ontology


    Beteiligte:
    Blasch, Erik (Autor:in) / Shen, Dan (Autor:in) / Chen, Genshe (Autor:in) / Sheaff, Carolyn (Autor:in) / Pham, Khanh (Autor:in)


    Erscheinungsdatum :

    06.03.2021


    Format / Umfang :

    5046664 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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