Conflicting evidence combination method based on evidence distance and belief entropy

Abstract

Dempster Shafer evidence theory is widely used in the field of information fusion. However, when there is a high conflict between the evidence, Dempster Shafer fusion method will generate a counter intuitive result. To address this issue, by considering the credibility and uncertainty information of the evidence, we propose a new multi-sensor data fusion method based on Hellinger distance and belief entropy. The new multi-sensor data fusion method consists of three main procedures. Firstly, the probability transformation method is used to transform the basic probability assignment into the probability distribution, then the Hellinger distance is utilized to measure the distance between the evidence, and the credibility of the evidence is calculated by the distance between the evidence. Secondly, considering the information volume of the evidence. In this paper, belief entropy is applied to measure the information volume of the evidence, and then the information volume of the evidence is used to modify the credibility of the evidence. Finally, the credibility of the evidence is taken as a weight factor to modify the original evidence to obtain the weighted average evidence, and then the weighted average evidence is fused with Dempster Shafer combination rule to achieve the final fusion result. Numerical examples and fault diagnosis applications illustrate the effectiveness and accuracy of the proposed method.