Complex Belief Divergence Measures for Multisource Information Fusion

Uncertainty is a crucial aspect in real-life scenarios, especially when dealing with ambiguous information from multimodal data sources. Complex evidence theory (CET), a generalized form of Dempster-Shafer evidence theory that uses complex numbers to describe uncertainty, offers a more promising framework for dealing with uncertainty in many fields. Focusing on conflict management in the CET framework, new divergence measures, namely, complex belief Kullback-Leibler (CBKL) divergence and complex belief Jensen-Shannon (CBJS) divergence, are proposed in this article to quantify the discrepancy between complex evidence bodies. Additionally, novel multisource information fusion methods based on CBKL divergence and CBJS divergence for decision making are proposed. To validate the effectiveness of the proposed methods, classification tasks are performed. The results demonstrate the feasibility and accuracy of the proposed methods in handling complex evidence fusion and decision-making tasks.