On the application of credibilistic filtering to uncertainty quantification and assessment in the operational space domain

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Bever, Marcus Jonathan

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The local space environment is a rapidly changing political and commercial landscape. There is a tremendous need for informed operational decisions in a regime that is fundamentally a collaborative and interconnected domain. These choices must be made not in spite of inaccurate and imprecise knowledge, but accounting for such restrictions in reasoning, thereby enabling the most realistic and actionable conclusions to be drawn in the realm of space traffic. A survey of hierarchical uncertainty methods yields a class of outer probability measures that is particularly well-suited to an adaptation of the Bayesian framework. Examples of an elementary nature are first explored where the delineations between aleatory and epistemic uncertainty is apparent. Potential and proven applications to the field of space situational awareness serve as the more complex realization of and motivation for such efforts. This study culminates in the formulation, demonstration, and analysis of the credibilistic Gaussian mixture filter in the context of space tracking


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