Shi Xin, Liang Fei, Qin Pengjie, Yu Liang, He Gaojie
School of Automation, Chongqing University, Chongqing 400044, China.
Entropy (Basel). 2023 Jun 16;25(6):948. doi: 10.3390/e25060948.
Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%.
证据理论被广泛用于处理不确定信息的融合，但冲突证据的融合仍是一个悬而未决的问题。为解决单目标识别中冲突证据融合的问题，我们提出了一种基于改进信度概率函数的新型证据组合方法。首先，改进的信度概率函数可以根据基本概率分配（BPA）中单子集命题的权重重新分配多子集命题的概率，这降低了转换过程中的计算复杂度和信息损失。提出结合曼哈顿距离和证据角度测量来提取证据确定性并获得各证据之间的相互支持信息；然后，利用熵来计算证据的不确定性，并采用加权平均法对原始证据进行修正和更新。最后，使用邓普斯特组合规则对更新后的证据进行融合。通过单子集命题和多子集命题高度冲突证据示例的分析结果验证，与约塞尔梅距离法、兰斯距离和可靠性熵组合法以及约塞尔梅距离和不确定性测度组合法相比，我们的方法具有更好的收敛性，平均准确率提高了0.51%和2.43%。
