A novel belief Tanimoto coefficient with its applications in multisource information fusion

Dempster-Shafer evidence theory (DST) is a versatile framework for handling uncertainty and provides a reliable method for data fusion. Managing conflicts between multiple bodies of evidence (BOEs) within DST poses a challenging problem that necessitates effective strategies. In this paper, we present a novel similarity measurement called the belief Tanimoto coefficient (BTC). The BTC accurately quantifies the consistency between BOEs by considering both the length and direction of the evidence vectors. Furthermore, we propose a conflict measurement approach based on BTC. We analyze and demonstrate the desirable properties of the proposed similarity and conflict measures. Numerical examples and comparisons are provided to illustrate the superior effectiveness and validity of BTC. Additionally, we introduce a multisource information fusion method called BTC-MSIF. The proposed BTC-MSIF method achieves higher accuracy rates compared to existing approaches in real-world scenarios, including fault diagnosis and pattern classification.