A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks
Information fusion using evidence theory in wireless sensors networks has been used extensively to identify targets because it offers the advantage of handling uncertainty. But the classical Dempster's combination rule cannot deal with highly conflicting information because it often generates c...
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| Online Access: | https://ieeexplore.ieee.org/document/8055558/ |
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doaj-c8793947bbbc496ba14e919c41ff01c02021-03-29T19:56:50ZengIEEEIEEE Access2169-35362017-01-015215852159610.1109/ACCESS.2017.27584198055558A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor NetworksYang Zhang0Yun Liu1https://orcid.org/0000-0002-3555-1932Zhenjiang Zhang2https://orcid.org/0000-0003-0217-3012Han-Chieh Chao3Jing Zhang4Qing Liu5Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, ChinaDepartment of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, ChinaCETC Key Laboratory of Aerospace Information Applications, Shijiazhuang, ChinaSchool of Information Science and Engineering, Fujian University of Technology, Fuzhou, China54th Research Institute of CETC, Shijiazhuang, ChinaPower Construction Corporation Of China Ltd., Beijing, ChinaInformation fusion using evidence theory in wireless sensors networks has been used extensively to identify targets because it offers the advantage of handling uncertainty. But the classical Dempster's combination rule cannot deal with highly conflicting information because it often generates counterintuitive results. In this paper, a new weighted evidence combination approach is proposed to solve this problem. First, two measures, i.e., a new contradiction measure of each body of evidence (BOE) and a probabilistic-based dissimilarity measure between two BOEs, are introduced to estimate the value of weight of each sensor. Then, when combining conflicting information, reasonable results can be produced by using weighted average of BOEs and Dempster's rule. Our experimental results showed that the proposed method has better performance in convergence than the existing methods.https://ieeexplore.ieee.org/document/8055558/Information fusionevidence theorypignistic probability functioncontradiction measuredissimilarity measurecombination rule |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Yang Zhang Yun Liu Zhenjiang Zhang Han-Chieh Chao Jing Zhang Qing Liu |
| spellingShingle |
Yang Zhang Yun Liu Zhenjiang Zhang Han-Chieh Chao Jing Zhang Qing Liu A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks IEEE Access Information fusion evidence theory pignistic probability function contradiction measure dissimilarity measure combination rule |
| author_facet |
Yang Zhang Yun Liu Zhenjiang Zhang Han-Chieh Chao Jing Zhang Qing Liu |
| author_sort |
Yang Zhang |
| title |
A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks |
| title_short |
A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks |
| title_full |
A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks |
| title_fullStr |
A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks |
| title_full_unstemmed |
A Weighted Evidence Combination Approach for Target Identification in Wireless Sensor Networks |
| title_sort |
weighted evidence combination approach for target identification in wireless sensor networks |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2017-01-01 |
| description |
Information fusion using evidence theory in wireless sensors networks has been used extensively to identify targets because it offers the advantage of handling uncertainty. But the classical Dempster's combination rule cannot deal with highly conflicting information because it often generates counterintuitive results. In this paper, a new weighted evidence combination approach is proposed to solve this problem. First, two measures, i.e., a new contradiction measure of each body of evidence (BOE) and a probabilistic-based dissimilarity measure between two BOEs, are introduced to estimate the value of weight of each sensor. Then, when combining conflicting information, reasonable results can be produced by using weighted average of BOEs and Dempster's rule. Our experimental results showed that the proposed method has better performance in convergence than the existing methods. |
| topic |
Information fusion evidence theory pignistic probability function contradiction measure dissimilarity measure combination rule |
| url |
https://ieeexplore.ieee.org/document/8055558/ |
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