A Gaussian Kernel Similarity Approach to Multisource Information Fusion Considering the Weight of Focal Element Beliefs

Abstract

Similarity has been extensively utilized to measure the degree of conflicts between evidences in multisource information fusion. The existent works, however, assumed that the contribution of each focal element’s belief to the similarity measure is the same, and the influence of the weight of focal element belief is not considered, which is unreasonable. This article proposes a new Gaussian kernel similarity approach to measure the similarity between evidences. The proposed Gaussian kernel similarity coefficient can effectively take account of the weights of focal element beliefs. In addition, it possesses some preferable properties, such as, bounded, consistent, and symmetrical. A multisource information fusion method based on the Gaussian kernel similarity coefficient is, therefore, investigated. The developed method mainly contains three steps: (1) The Gaussian kernel similarity coefficient, as a connection, is leveraged to calculate the weight of evidences based on the weight of focal element beliefs; (2) The initial evidences are, thereby, modified based on the weight of evidence via the weight-average method; and (3) The final multisource information fusion can be achieved by the Dempster’s combination rule using the modified evidences. Two illustrative examples with singletons and multi-element subsets are presented, and it is verified that the proposed method is effective in dealing with conflicting evidences.