A new method to measure the divergence in evidential sensor data fusion
Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other. Recent researches show that weighting the evi...
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| Format: | Article |
| Language: | English |
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Wiley
2019-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719841295 |
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| _version_ | 1849308028187181056 |
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| author | Yutong Song Yong Deng |
| author_facet | Yutong Song Yong Deng |
| author_sort | Yutong Song |
| collection | DOAJ |
| description | Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other. Recent researches show that weighting the evidences with the consideration of its corresponding credibility is an efficient methodology. As a result, how to determine the weight is an important issue. In this article, a new divergence measure of BPA is proposed based on geometric mean of Deng relative entropy. The weight of each evidence is determined by the proposed divergence measure and information volume. Compared with the existing belief Jensen–Shannon divergence, the proposed method has a better performance in the convergence to the correct target. The result shows that the proposed method outperforms other related methods, giving the highest belief value 98.98% to the correct target. |
| format | Article |
| id | doaj-art-16e1990fdb344a58808dfa1790dcb7aa |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-16e1990fdb344a58808dfa1790dcb7aa2025-08-20T03:54:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-04-011510.1177/1550147719841295A new method to measure the divergence in evidential sensor data fusionYutong Song0Yong Deng1Glasgow College, University of Electronic Science and Technology of China, Chengdu, ChinaInstitute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, ChinaEvidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other. Recent researches show that weighting the evidences with the consideration of its corresponding credibility is an efficient methodology. As a result, how to determine the weight is an important issue. In this article, a new divergence measure of BPA is proposed based on geometric mean of Deng relative entropy. The weight of each evidence is determined by the proposed divergence measure and information volume. Compared with the existing belief Jensen–Shannon divergence, the proposed method has a better performance in the convergence to the correct target. The result shows that the proposed method outperforms other related methods, giving the highest belief value 98.98% to the correct target.https://doi.org/10.1177/1550147719841295 |
| spellingShingle | Yutong Song Yong Deng A new method to measure the divergence in evidential sensor data fusion International Journal of Distributed Sensor Networks |
| title | A new method to measure the divergence in evidential sensor data fusion |
| title_full | A new method to measure the divergence in evidential sensor data fusion |
| title_fullStr | A new method to measure the divergence in evidential sensor data fusion |
| title_full_unstemmed | A new method to measure the divergence in evidential sensor data fusion |
| title_short | A new method to measure the divergence in evidential sensor data fusion |
| title_sort | new method to measure the divergence in evidential sensor data fusion |
| url | https://doi.org/10.1177/1550147719841295 |
| work_keys_str_mv | AT yutongsong anewmethodtomeasurethedivergenceinevidentialsensordatafusion AT yongdeng anewmethodtomeasurethedivergenceinevidentialsensordatafusion AT yutongsong newmethodtomeasurethedivergenceinevidentialsensordatafusion AT yongdeng newmethodtomeasurethedivergenceinevidentialsensordatafusion |