A new method to measure the divergence in evidential sensor data fusion

Yutong Song · Yong Deng

104 citations0/0 files storedInternational Journal of Distributed Sensor Networks

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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.

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References

2000Combining belief functions when evidence conflicts 2018Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy 2004Combining belief functions based on distance of evidence 2016Deng entropy 2018A correlation coefficient for belief functions 2018A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers 2018An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors 2018Zero-sum polymatrix games with link uncertainty: A Dempster-Shafer theory solution

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