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

摘要/Abstract

摘要：

传感器中存在大量重复、冲突、冗余数据，传统数据融合方法只能融合部分源数据，得出的目标监测数据可信度依旧较低，为此，本文提出一种基于模糊理论的多源异构传感器数据融合模型。利用D-S证据理论设计数据融合规则，通过概率分配函数、距离矩阵降低多源数据融合规则计算难度，采用二值型函数转换各数据源，使用支持度函数算出各数据源的支持度值，借助模糊理论量化算子得出OWA算子权重值，根据这两个值将冲突数据去除，重复数据融合，完成数据源融合。实验结果表明，本文方法能够有效降低各源数据融合误差，提升监测数据的可靠性，并能保证融合时间开销最短。

关键词: 模糊理论, D-S证据理论, 多源异构传感器, 多源数据融合, 支持度, 融合规则

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

中图分类号:

TN01

杨秋菊. 基于模糊理论的多源异构传感器数据融合模型[J]. 吉林大学学报(工学版), 2024, 54(10): 3058-3063.

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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时间/s	H1	H2	H3	H4	H5	H6	H7	H8
1	28.0	27.5	27.9	27.2	28.3	28.4	29.0	27.9
2	28.8	28.7	27.9	28.3	28.7	27.6	28.1	28.6
3	28.3	27.8	28.4	28.3	27.6	28.9	29.8	29.7
4	27.8	28.7	28.3	29.7	29.0	28.6	28.1	27.9
5	28.3	27.3	28.1	27.4	27.6	28.7	29.6	29.5
6	28.1	27.2	29.7	28.3	27.1	27.9	27.8	29.3
7	29.3	28.7	29.5	29.8	27.3	28.6	28.6	29.7
8	27.3	27.8	28.6	29.5	27.6	28.7	27.6	28.5
9	29.3	27.2	29.7	27.9	29.0	27.6	27.8	28.3
10	28.3	27.7	28.0	27.3	27.3	29.5	27.6	29.7

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