Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence

被引：28
|
作者：
Zhao, Guangzhe [1 ]
Chen, Aiguo [2 ]
Lu, Guangxi [2 ]
Liu, Wei [2 ]
机构：
[1] Beijing Univ Civil Engn & Architecture, Beijing 102616, Peoples R China
[2] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China
来源：
TSINGHUA SCIENCE AND TECHNOLOGY | 2020年 / 25卷 / 01期
基金：
中国国家自然科学基金;
关键词：
data fusion; fuzzy sets; Dempster-Shafer (D-S) theory; AGGREGATION; TRACKING;
D O I：
10.26599/TST.2018.9010138
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer (D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.
引用
收藏
页码：12 / 19
页数：8
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