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Sensors 2017, 17(4), 928; doi:10.3390/s17040928

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Author to whom correspondence should be addressed.
Academic Editors: Maria Gabriella Xibilia, Alex Alexandridis and Elias N. Zois
Received: 9 March 2017 / Revised: 18 April 2017 / Accepted: 20 April 2017 / Published: 22 April 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
View Full-Text   |   Download PDF [337 KB, uploaded 22 April 2017]   |  


In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor. View Full-Text
Keywords: uncertainty measure; Dempster–Shafer evidence theory; Deng entropy; weighted belief entropy; sensor data fusion uncertainty measure; Dempster–Shafer evidence theory; Deng entropy; weighted belief entropy; sensor data fusion

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Tang, Y.; Zhou, D.; Xu, S.; He, Z. A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion. Sensors 2017, 17, 928.

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