An Improved Method to Manage Conflict Data Using Elementary Belief Assignment Function in the Evidence Theory

Abstract

Dempster-Shafer evidence theory plays an important role in many applications such as multi-sensor data fusion and pattern recognition. However, if there are conflicts among evidences, the results of data fusion using Dempster combination rule may lead to counter-intuitive results. In this paper, we propose a new method named elementary belief assignment function for conflict data fusion. The proposed method aims at getting a more rational data fusion result by preprocessing the mass function before implementing data fusion with Dempster's combination rule. The elementary belief assignment function takes into consideration not only the number of focal elements in the current body of evidence but also the proposition in the power set space. By assigning the mass value of potential conflict focal element to other related propositions in the power set space, we can reduce the conflict level among different bodies of evidences effectively. We verify the rationality and efficiency of the proposed method according to several experiment examples.