Integrated Data Fusion Using Dempster-Shafer Theory

被引：1

作者：

Zhang, Yang ^{[1
]}

Zeng, Qing-An ^{[2
]}

Liu, Yun ^{[1
]}

Shen, Bo ^{[3
]}

机构：

[1] Beijing Jiaotong Univ, Beijing Municipal Commiss Educ, Beijing Key Lab Commun & Informat, Beijing, Peoples R China

[2] North Carolina A&T State Univ, Dept Comp Syst Technol, Greensboro, NC USA

[3] Beijing Jiaotong Univ, Natl Engn Lab Mobile Internet Syst & Applicat, Beijing, Peoples R China

来源：

2015 FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE THEORY, SYSTEMS AND APPLICATIONS (CCITSA 2015) | 2015年

关键词：

data fusion; D-S evidence theory; conflicting data; reliability coefficient;

D O I：

10.1109/CCITSA.2015.25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes an integrated data fusion approach. The approach is based on the Dempster-Shafer evidence theory, and includes four main aspects: the construction of basic probability assignment, a novel reliability coefficient function converting similarity to initial weight factors, an improved fusion approach by reassigning reliability coefficient, and the "Discount Rule." Utilizing the integrated approach, conflicting data are fused more accurately and effectively than using the single fusion method. Experimental results show that the belief assignment results of the proposed approach are in accordance with the practical situation.

引用

页码：98 / 103

页数：6

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