Summary of Treatment Of Epistemic Uncertainty in Conjunction Analysis with Dempster-shafer Theory, by Luis Sanchez and Massimiliano Vasile and Silvia Sanvido and Klaus Mertz and Christophe Taillan
Treatment of Epistemic Uncertainty in Conjunction Analysis with Dempster-Shafer Theory
by Luis Sanchez, Massimiliano Vasile, Silvia Sanvido, Klaus Mertz, Christophe Taillan
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Theory (cs.IT); Probability (math.PR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach to modeling epistemic uncertainty in Conjunction Data Messages (CDM) and classifying conjunction events based on confidence in probability of collision uses Dempster-Shafer Theory (DSt). The method starts with unknown distributions drawn from a family, using the Dvoretzky-Kiefer-Wolfowitz (DKW) inequality to construct robust bounds. A DSt structure is derived from probability boxes constructed with DKW inequality, encapsulating uncertainty in CDMs and allowing computation of belief and plausibility in realizing a given probability of collision. The methodology is tested on real events and compared against existing practices in the European and French Space Agencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to deal with uncertain information in space missions. It uses special math tools to figure out how likely it is that two objects will collide. This helps make better decisions about what to do next. The method is tested on real events and compared to what others are doing. It shows a more conservative approach, but also provides extra information about uncertainty. |
Keywords
» Artificial intelligence » Probability
