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A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

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Abstract

Multi-sensor data fusion (MSDF) problems have attracted widespread attention recently. However, it is still an open issue about how to make the fusion process effectively even if the collected data conflict due to several unpredictable reasons. Moreover, most existing approaches mainly concentrated on the distinction of evidence sources, which cannot well consider the feature of individual belief degree and the associated preference of decision-makers. To address such an issue, a dynamic MSDF method based on evidence theory and weighted ordered weighted averaging (WOWA) operator is proposed in this study. A numerical example is analyzed to demonstrate its whole calculation procedure. Two simulation experiments, composed of a motor rotor fault diagnosis and an insulator string target recognition application, are also mentioned to illustrate its effectiveness and applied value. The results show that the proposed methodology can enhance the fusion accuracy in the constrained scenarios with the consideration of preference relation.

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Acknowledgments

The authors greatly appreciate the reviews’ suggestions and the editor’s encouragement. This work was supported in part by the National Defense Innovation Science Foundation, in part by the National Natural Science Foundation of China under Grant 61174196, in part by the Innovation Foundation of Equipment Development Department and CASIC, and in part by the Fundamental Research Funds for the Central Universities under Grant 2042019kf0220.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, Hubei, 430072, China

    Jiayi Wang

  2. School of Electronic Information, Wuhan University, Wuhan, Hubei, 430072, China

    Qiuze Yu

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  1. Jiayi Wang
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Correspondence to Qiuze Yu.

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Wang, J., Yu, Q. A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator. Appl Intell 50, 3837–3851 (2020). https://doi.org/10.1007/s10489-020-01739-8

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