 A new method to measure the divergence in evidential sensor data fusionYutong Song and Yong Deng International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 4, 1550147719841295 Abstract: 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. Keywords: Dempster–Shafer evidence theory; belief function; divergence measure; relative belief entropy; Deng entropy; target recognition (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719841295 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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