Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach. (26th March 2015)

• Record Type: Journal Article
• Authors: Ma, Mengmeng; An, Jiyao
• Other Names: Campopiano Stefania Academic Editor.
• Is Part Of: Journal of sensors. Volume 2015(2015)
• Publication Date: 2015-03-26
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2015/509385
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but also the divergence degree of the hypothesis that two BBAs support. Finally, the weighting factors used to reassign conflicts on BBAs are developed and Dempster's rule is chosen to combine the discounted sources. Simple numerical examples are employed to demonstrate the merit of the proposed method. Through analysis and comparison of the results, the new combination approach can effectively solve the problem of conflict management with better convergence performance and robustness.