Abstract
In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.
Highlights
In the age of artificial intelligence, sensors play quite an important role for environment sensing and information acquisition
This paper focuses on multi-sensor data fusion by firstly proposing a new uncertainty measure and designing a new uncertainty measure-based sensor data fusion approach
In the Dempster–Shafer evidence theory framework, the weighted belief entropy is proposed based on Deng entropy
Summary
In the age of artificial intelligence, sensors play quite an important role for environment sensing and information acquisition. Multi-sensor modeling and sensor data fusion are important issues in many real applications [1,2,3,4,5,6,7]. Many methods have been proposed for multi-sensor modeling and sensor data fusion [8], including neural network models [1,9], belief function theory [10,11], Dempster–Shafer evidence theory [12,13,14], fuzzy set theory [15], Z-Numbers [16], and so on [17]. This paper focuses on multi-sensor data fusion by firstly proposing a new uncertainty measure and designing a new uncertainty measure-based sensor data fusion approach
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