Treatment of Epistemic Uncertainty in Conjunction Analysis with Dempster-Shafer Theory

Luis Sanchez, Massimiliano Vasile, Silvia Sanvido, Klaus Mertz, Christophe Taillan
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Information Theory (cs.IT), Probability (math.PR)
2024-01-28 00:00:00
The paper presents an approach to the modelling of epistemic uncertainty in Conjunction Data Messages (CDM) and the classification of conjunction events according to the confidence in the probability of collision. The approach proposed in this paper is based on the Dempster-Shafer Theory (DSt) of evidence and starts from the assumption that the observed CDMs are drawn from a family of unknown distributions. The Dvoretzky-Kiefer-Wolfowitz (DKW) inequality is used to construct robust bounds on such a family of unknown distributions starting from a time series of CDMs. A DSt structure is then derived from the probability boxes constructed with DKW inequality. The DSt structure encapsulates the uncertainty in the CDMs at every point along the time series and allows the computation of the belief and plausibility in the realisation of a given probability of collision. The methodology proposed in this paper is tested on a number of real events and compared against existing practices in the European and French Space Agencies.
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