Abstract
Multi-sensor data fusion technology based on Dempster–Shafer evidence theory is widely applied in many fields. However, how to determine basic belief assignment (BBA) is still an open issue. The existing BBA methods pay more attention to the uncertainty of information, but do not simultaneously consider the reliability of information sources. Real-world information is not only uncertain, but also partially reliable. Thus, uncertainty and partial reliability are strongly associated with each other. To take into account this fact, a new method to represent BBAs along with their associated reliabilities is proposed in this paper, which is named reliability-based BBA. Several examples are carried out to show the validity of the proposed method.
Highlights
In practical applications, there are various interferences in the working environment
Sensor data fusion technology can combine the related information from multiple sensors to enhance the robustness and safety of a system [1,2]
Possibility theory was introduced in 1978 by Zadeh [17]. It describes reasonably the meaning of information, especially the meaning of incomplete information within a possibilistic framework [18,19,20,21], which could be seen as the theory interconnecting fuzzy sets and evidence theory
Summary
There are various interferences in the working environment. Possibility theory was introduced in 1978 by Zadeh [17] It describes reasonably the meaning of information, especially the meaning of incomplete information within a possibilistic framework [18,19,20,21], which could be seen as the theory interconnecting fuzzy sets and evidence theory. In the μBBA method, the fuzzy membership function is used to represent the knowledge of possibility, and the proposition is modeled by an interval. Guo et al [40] presented a new framework for sensor reliability evaluation in classification problems based on evidence theory. The reliability of these methods is measured from the support degree (consistency) among BBA. The reliability is obtained based on the measure of sensor capability to distinguish the different targets.
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