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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.476~482

http://doi.org/10.1631/jzus.2005.A0476


A new fusion approach based on distance of evidences


Author(s):  CHEN Liang-zhou, SHI Wen-kang, DENG Yong, ZHU Zhen-fu

Affiliation(s):  School of Electronics & Information Technology, Shanghai Jiaotong University, Shanghai 200030, China; more

Corresponding email(s):   tiger-chen@sjtu.edu.cn

Key Words:  Data fusion, Evidence distance, Conflicting evidence, Evidence credibility, Combination rules


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CHEN Liang-zhou, SHI Wen-kang, DENG Yong, ZHU Zhen-fu. A new fusion approach based on distance of evidences[J]. Journal of Zhejiang University Science A, 2005, 6(5): 476~482.

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DOI - 10.1631/jzus.2005.A0476


Abstract: 
Based on the framework of evidence theory, data fusion aims at obtaining a single Basic Probability Assignment (BPA) function by combining several belief functions from distinct information sources. Dempster’s rule of combination is the most popular rule of combinations, but it is a poor solution for the management of the conflict between various information sources at the normalization step. Even when it faces high conflict information, the classical Dempster-Shafer’s (D-S) evidence theory can involve counter-intuitive results. This paper presents a modified averaging method to combine conflicting evidence based on the distance of evidences; and also gives the weighted average of the evidence in the system. Numerical examples showed that the proposed method can realize the modification ideas and also will provide reasonable results with good convergence efficiency.

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Reference

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[10] Shafer, G., 1976. A Mathematical Theory of Evidence. Princeton University Press.

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[12] Smets, P., 1993. Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. Int J Approx Reason, 9:1-35.

[13] Voorbraak, F., 1991. On the justification of Dempster’s rule of combination. Artificial Intelligence, 48:253-286.

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[15] Zadeh, L., 1986. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Mag, 7(1):85-90.

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