Full Text:   <3478>

CLC number: TP274

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 0000-00-00

Cited: 8

Clicked: 7029

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

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


Share this article to: More <<< Previous Article|

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.

@article{title="A new fusion approach based on distance of evidences",
author="CHEN Liang-zhou, SHI Wen-kang, DENG Yong, ZHU Zhen-fu",
journal="Journal of Zhejiang University Science A",
volume="6",
number="5",
pages="476-482",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0476"
}

%0 Journal Article
%T A new fusion approach based on distance of evidences
%A CHEN Liang-zhou
%A SHI Wen-kang
%A DENG Yong
%A ZHU Zhen-fu
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 5
%P 476-482
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0476

TY - JOUR
T1 - A new fusion approach based on distance of evidences
A1 - CHEN Liang-zhou
A1 - SHI Wen-kang
A1 - DENG Yong
A1 - ZHU Zhen-fu
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 5
SP - 476
EP - 482
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Dempster, A., 1967. Upper and lower probabilities induced by a multi-valued mapping. Ann. Math. Stat., 38:325-339.

[2] Dubois, D., Prade, H., 1998. Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, 4:244-264.

[3] Goodman, I., Mahler, R.P.S., Nguyen, H.T., 1997. Mathematics of Data Fusion. Kluwer Academic Publishers, Dordrecht.

[4] Hall, D.L., Linas, J., 2001. Handbook of Multisensor Data Fusion. CRC Press.

[5] Jousselme, A.L., Grenier, D., Bosse, E., 2001. A new distance between two bodies of evidence. Information Fusion, 2:91-101.

[6] Lefevre, E., Colot, O., Vannoorenberghe, P., 2002. Belief function combination and conflict management. Information Fusion, 3:149-162.

[7] Linas, J., Waltz, E., 1990. Multisensor Data Fusion. Artech House, Massachusetts.

[8] Murphy, C.K., 2000. Combining belief functions when evidence conflicts. Decisions Support Systems, 29:1-9.

[9] Pearl, J., 1990. Reasoning with belief functions: an analysis of compatibility. Int J of Approx Reason, 4:636-389.

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

[11] Smets, P., 1990. The combination of evidence in the transferable belief model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5):447-458.

[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.

[14] Yager, R.R., 1986. On the Dempster-Shafer framework and new combination rules. Information Science, 41(2):93-137.

[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.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE