标题:Multisensor Data Fusion Based on Modified D-S Evidence Theory
作者:Zhou, Yingming; Xu, Hongji; Sun, Junfeng; Pan, Lingling; Du, Baozhen; Chen, Min
通讯作者:Xu, HJ
作者机构:[Zhou, Yingming; Xu, Hongji; Sun, Junfeng; Pan, Lingling; Du, Baozhen; Chen, Min] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R C 更多
会议名称:International Conference on Computer Modeling, Simulation and Algorithm (CMSA)
会议日期:APR 22-23, 2018
来源:PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON COMPUTER MODELING, SIMULATION AND ALGORITHM (CMSA 2018)
出版年:2018
卷:151
页码:324-327
关键词:Dempster-Shafer (D-S) evidence theory; multisensor data fusion;; information gain; fuzzy preference relations
摘要:Dempster-Shafer (D-S) evidence theory has been widely used in multisensor data fusion to deal with uncertain information. But unreasonable results may be produced by using D-S combination rule in the case of that data are conflicting with each other. This paper proposes a modified evidence combination method based on information gain and fuzzy preference relations. This method takes account of both historical data and real-time data by introducing the concepts of historical support and real-time support, so it can obtain more accurate results by using more effective information. In order to evaluate the performance of the proposed evidence combination method, an example of classifying the patient's state by five vital signs is given in this paper. The simulation experiment shows that the proposed modified method achieves higher classification accuracy compared with other three data fusion methods.
收录类别:CPCI-S
资源类型:会议论文
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