A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator
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A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator Jiayi Wang1 · Qiuze Yu2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Evidence theory · Multi-sensor data fusion · Weighted ordered weighted averaging operator · Dynamic · Preference
1 Introduction Multi-sensor data fusion (MSDF) has been widely utilized in many applications. With the rapid progress of sensor technology, several scholars have carried out a large number of related studies [1, 2]. In real-life scenarios, several types of data can be collected from different sensors. To identify the target object more effectively, the data fusion process is considered to be one of the most significant procedures. Considering the complexity in actual situations, the uncertainty contained in collected data deserves to be well modeled. Recently, some techniques have been widely utilized to measure uncertain information such as fuzzy sets [3, 4], D number [5, 6], Z number [7, 8] and evidence theory [9, 10]. One of the most common techniques among them is called as Dempster-Shafer evidence theory (DSET). Based Qiuze Yu
[email protected] 1
School of Computer Science, Wuhan University, Wuhan, Hubei, 430072, China
2
School of Electronic Information, Wuhan University, Wuhan, Hubei, 430072, China
on its strong advantages in complex event processing, DSET has been utilized in several essential applications like decision-making [11, 12] and fault diagnosis [13, 14]. As an effective tool, DSET has been discussed its application for solving MSDF problems with the utilization of combination rule and other techniques, which is also widely regarded as a kind of decision-level fusion process [15]. Sensors need to perform basic processing locally, including preprocessing, feature extraction, recognition or judgment to establish a preliminary conclusion on the observed target. Then the decision-level fusion decision is completed through association processing to eventually o
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