A new belief divergence measure for Dempster–Shafer theory based on belief and plausibility function and its application in multi-source data fusion. (January 2021)

• Record Type: Journal Article
• Authors: Wang, Hongfei; Deng, Xinyang; Jiang, Wen; Geng, Jie
• Is Part Of: Engineering applications of artificial intelligence. Volume 97(2021)
• Publication Date: 2021-01
• Subjects: Dempster–Shafer theory (DST) -- Belief divergence measure -- Data fusion -- Evidential conflict; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104030
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Dempster–Shafer theory (DST) has extensive and important applications in information fusion. However, when the evidences are highly conflicting with each other, the Dempster's combination rule often leads to a series of counter-intuitive results. In this paper, we propose a new belief divergence measure for DST, which can reflect the correlation of different kinds of subsets by taking into account the belief measure and plausibility measure of mass function. Furthermore, the proposed divergence measure has the properties of boundedness, non-degeneracy and symmetry. In addition, a new multi-source data fusion method is proposed based on the proposed divergence measure. This method utilizes not only the credibility weights but also the information volume weights to determine the comprehensive weights of evidences, which can fully reflect the relationship between evidences. Application cases and simulation results show that the proposed method is reasonable and effective.