Treatment of Epistemic Uncertainty in Conjunction Analysis with Dempster-Shafer Theory
This addresses uncertainty quantification for space agencies in collision risk analysis, but it is incremental as it builds on existing Dempster-Shafer methods with specific applications.
The paper tackled the problem of modeling epistemic uncertainty in Conjunction Data Messages (CDM) for space collision risk assessment by using Dempster-Shafer Theory and the DKW inequality to compute belief and plausibility in collision probabilities. It showed that the proposed classification system is more conservative than the European Space Agency's approach and provides added uncertainty quantification, as tested on real events.
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. We will show that the classification system proposed in this paper is more conservative than the approach taken by the European Space Agency but provides an added quantification of uncertainty in the probability of collision.