New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion

Multisource information fusion technology significantly benefits from using information across various sources for decision-making, particularly by leveraging evidence theory to manage uncertain information efficiently. Nonetheless, dealing with highly conflicting evidence presents a considerable challenge. To tackle this issue, this paper introduces a new belief divergence measure within the framework of evidence theory. The proposed measure, which incorporates the cosine function and pignistic probability transformation, is adept at quantifying the disparity between the evidences while maintaining key properties, such as boundedness, non-degeneracy and symmetry. Moreover, building upon the concepts of proposed belief divergence and belief entropy, this paper further proposes a new fusion method that employs a weighted evidence average prior to the application of Dempster’s rule. The performance of the proposed method is validated on several applications, and the results demonstrate its superior ability to absorb highly conflicting evidence compared with existing methods.