Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach

Mengmeng Ma · Jiyao An

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Multi-Criteria Decision Making Target Tracking and Data Fusion in Sensor Networks Fuzzy and Soft Set Theory

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.

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References

1996Fuzzy sets 1987On the dempster-shafer framework and new combination rules 2000Combining belief functions when evidence conflicts 2001A new distance between two bodies of evidence 2004Decision making in the TBM: the necessity of the pignistic transformation 2004Combining belief functions based on distance of evidence 2002Belief function combination and conflict management 2005Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory

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