Discussion of Dempster-Shafer Theory and its Application to Identification Fusion.

摘要

This paper outlines some of the basics of Dempster-Shafer Theory, which is a mathematical theory for combining evidence from different sources to obtain a degree of belief in a proposition. In particular, different combination rules available within the context of Identification Fusion and the assumptions and implications of each of those rules are outlined and investigated. However, the belief function arising from combining evidence under Dempster-Shafer Theory is often insufficient to support decision-making, and a transformation from the belief function to a probability distribution is required. Several different transformations and illustrative examples implementing Dempster-Shafer Theory for Identification Fusion are provided. The results and some possible directions for future work are discussed.