Abstract: Dempster-Shafer evidence theory is widely used in many fields of information fusion. However, the counter-intuitive results may be obtained when combining with highly conflicting evidence. To deal with such a problem, this paper puts forward a new method based on the distance of evidence and the uncertainty measure. First, based on the distance of evidence, the evidence is divided into two parts, the credible evidence and the incredible evidence. Then, a novel belief entropy is applied to measure the information volume of the evidence. Finally, the weight of each evidence is obtained and used to modify the evidence before using the Dempster’s combination rule. Numerical examples show that the proposed method can effectively handle conflicting evidence with better convergence.

Key words: evidence theory, conflict, belief entropy, evidence distance

摘要： Dempster-Shafer证据理论广泛应用于信息融合的许多领域。但是，当使用证据理论对高度冲突的数据进行融合时，此时会出现有违常理的结果。为了解决冲突数据融合的问题，提出了一种基于证据距离和不确定度的冲突数据融合方法。通过证据距离计算证据之间的相对距离，将证据分为两种类别：可信证据和不可信证据。再应用新提出的信度熵对证据不确定度进行度量，对每个证据分配相应的权重，再根据权重对每个证据的基本信度值进行修正，再运用Dempster融合规则对修正后的信度进行组合得到最终全局信度值。通过算例实验，与其他几种经典的数据融合算法进行对比，仿真结果证明算法能够有效地解决数据冲突的问题。

关键词: 证据理论, 冲突, 信度熵, 证据距离