The Multisensor Data Fusion Method Based on Improved Fuzzy Evidence Theory in the Coal Mine Environment

Abstract

An enhanced evidence theory-based multisensor data fusion technique is presented to address the problem of poor data fusion caused by an unknown interference in the fully automated mining face multisensor system of a coal mine. Initially, the set of all measurement values is considered as the identification framework, and the principles of fuzzy mathematics are applied to introduce the membership function. This leads to the proposal of a novel method for calculating mutual support among multiple sensors. Furthermore, the basic belief assignment (BBA) in evidence theory is determined by measuring the confidence distance between sensors. Subsequently, a divergence measure is employed to assess the level of conflict and difference between BBA functions, which serves as an indicator of their credibility. The credibility of BBA functions is further adjusted by calculating their information volume using Shannon entropy. This adjustment aims to increase the weight of BBA functions that exhibit less conflict with other BBA functions. Ultimately, the fusion result is obtained through an evidence combination rule based on a conflict allocation. The numerical experimental results demonstrate that the proposed approach achieves higher accuracy, better robustness, and generality compared to the existing methods.