Journal
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
Volume 14, Issue 3, Pages 329-343Publisher
CCC PUBL-AGORA UNIV
DOI: 10.15837/ijccc.2019.3.3589
Keywords
Dempster-Shafer evidence theory; sensor data fusion; fault diagnosis; evidence distance; belief entropy; information volume
Funding
- National Natural Science Foundation of China [61573290, 61503237]
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Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.
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