基于模糊理论的多源异构传感器数据融合模型

doi:10.13229/j.cnki.jdxbgxb.20230554

Abstract

Abstract:

There is a large amount of duplicate， conflicting， and redundant data in sensors， and traditional data fusion methods can only fuse partial source data， resulting in low credibility of target monitoring data. Therefore， a multi-source heterogeneous sensor data fusion model based on fuzzy theory is proposed. The D-S evidence theory is used to design data fusion rules， and the probability distribution function and distance matrix are used to reduce the calculation difficulty of multi-source data fusion rules. The binary function is used to convert each data source， and the support function is used to calculate the support value of each data source. The weight value of OWA operator is obtained with the help of fuzzy theory quantitative operator. According to these two values， conflicting data is removed， duplicate data is fused， and data source fusion is completed. The experimental results show that the proposed method can effectively reduce the fusion error of various source data， improve the reliability of monitoring data， and ensure the shortest fusion time cost.

Key words: fuzzy theory, D-S evidence theory, multi source heterogeneous sensors, multi source data fusion, support level, fusion rules

CLC Number:

TN01

Qiu-ju YANG. Multisource heterogeneous sensor data fusion model based on fuzzy theory[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 3058-3063.

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Table 1

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References 20

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Multisource heterogeneous sensor data fusion model based on fuzzy theory

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