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.
Highlights
Information fusion technology has been applied to a variety of scenarios, such as military situation assessment, artificial intelligence, cloud computing and so on
Information fusion needs to deal with a large number of uncertain information with the characteristics of multi-source heterogeneity, imprecision, unreliability and imperfection
As a typical information fusion theory, D-S evidence theory is an extension of probability theory, that the basic probability assignment (BPA) function is a kind of imprecise probability [9]
Summary
Information fusion technology has been applied to a variety of scenarios, such as military situation assessment, artificial intelligence, cloud computing and so on. The negation of BPA is a new mathematical tool for uncertain or conflict information processing in evidence theory [30]. R. Li et al.: Improved Method to Manage Conflict Data Using Elementary Belief Assignment Function in the Evidence Theory the conflict information in the frame of discernment through logical operation to avoid conflicts in subsequent data fusion [34]. To address the conflict data fusion problem in evidence theory, current researches focus on modifying the Dempster’s combination rule or modify the original mass function [35]–[37]. The method of modifying origin mass function retains the characteristics of the classic Dempster combination rule. Base belief function modifies the origin mass function which is a new perspective for conflict data processing.
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