A Robust Evidential Multisource Data Fusion Approach Based on Cooperative Game Theory and Its Application in EEG

Multisource data fusion analysis, particularly in decision-level fusion strategies, is emerging for application in real-life scenarios. The Dempster-Shafer evidence theory (DSET) is a prevalent approach that has significant importance in managing the fusion tasks. However, existing fusion approaches have limitations in dealing with redundant information and computational complexity associated with the fusion procedure. Though conflict management has been thoroughly studied, other limitations have not been well addressed. In this article, we propose a novel approach for evidential multisource data fusion based on game-theoretic analysis. The introduction of the Shapley function considers the interaction effect of focal elements, mitigating the negative influence of redundant evidence. Additionally, the computational complexity of the fusion procedure is reduced to the same level as the approximate Bayesian update model. We provide a numerical example with conflicting and redundant evidence to show that the proposed approach outperforms current advanced weighted average-based fusion methods. Moreover, a simulation experiment demonstrates the practicality and effectiveness of the proposed approach in identifying driver fatigue states based on electroencephalography (EEG) signals.

Automated summary

This article proposes a novel approach for evidential multisource data fusion based on game-theoretic analysis. By introducing the Shapley function, the negative influence of redundant evidence is mitigated, and the computational complexity of the fusion procedure is reduced.