Possibilistic space object tracking under epistemic uncertainty

Bayesian filtering is a popular class of estimation algorithms for addressing the space object tracking problem. Bayesian filters assume a random physical system with known statistics of various uncertainty sources. The major challenge is that the exact knowledge of some random process may not be available for analysis, preventing us from performing a probabilistic characterization of the epistemic uncertainty components. In this paper, we explore the use of the Outer Probability Measures (OPMs) to achieve a faithful uncertainty representation derived from all available yet imperfect information in the process of space object tracking. Leveraging the concepts of OPMs, a refined Possibilistic Admissible Region approach is proposed, in which the initial orbital state is modeled using a novel parameter estimation method. The OPM filter is employed to integrate different types of data sources in the presence of assumed ignorance. The efficacy of the developed method is validated by several space object tracking scenarios using real radar measurements and two-line elements data.