文章基本信息

标题：An improved belief entropy–based uncertainty management approach for sensor data fusion
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作者：Yongchuan Tang ; Deyun Zhou ; Zichang He 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：7
页码：1
DOI：10.1177/1550147717718497
出版社：Hindawi Publishing Corporation
摘要：In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy–based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster–Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertainty measure is expressed as the improved belief entropy. Evidence modification of conflict sensor data is based on the proposed uncertainty management approach before evidence fusion with Dempster’s rule of combination. Finally, the fusion result can be applied in real applications. A case study on sensor data fusion for fault diagnosis is presented to show the rationality of the proposed method.
关键词：Dempster–Shafer evidence theory; belief entropy; Deng entropy; improved belief entropy; uncertainty management; sensor data fusion