Abstract
Dempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, how to avoid the counter-intuitive results is an open issue when combining highly conflicting pieces of evidence. In order to handle such a problem, a weighted combination method for conflicting pieces of evidence in multi-sensor data fusion is proposed by considering both the interplay between the pieces of evidence and the impacts of the pieces of evidence themselves. First, the degree of credibility of the evidence is determined on the basis of the modified cosine similarity measure of basic probability assignment. Then, the degree of credibility of the evidence is adjusted by leveraging the belief entropy function to measure the information volume of the evidence. Finally, the final weight of each piece of evidence generated from the above steps is obtained and adopted to modify the bodies of evidence before using Dempster’s combination rule. A numerical example is provided to illustrate that the proposed method is reasonable and efficient in handling the conflicting pieces of evidence. In addition, applications in data classification and motor rotor fault diagnosis validate the practicability of the proposed method with better accuracy.
Highlights
Multi-sensor data fusion technology has received significant attention in a variety of fields, as it combines the collected information from multi-sensors, which can enhance the robustness and safety of a system
Dempster–Shafer evidence theory is effective to model both of the uncertainty and imprecision without prior information, so it is widely applied in various fields for information fusion [29,30,31,32]
We focus on decision-level fusion, and try to improve the performance of the system based on Dempster–Shafer evidence theory
Summary
Multi-sensor data fusion technology has received significant attention in a variety of fields, as it combines the collected information from multi-sensors, which can enhance the robustness and safety of a system. Dempster–Shafer evidence theory is effective to model both of the uncertainty and imprecision without prior information, so it is widely applied in various fields for information fusion [29,30,31,32] It may result in counter-intuitive results when combining highly conflicting pieces of evidence [33]. In this paper, a weighted combination method for conflicting pieces of evidence in multi-sensor data fusion is proposed to resolve fusion problem of highly conflicting evidence. The modified credibility degree of each piece of evidence is used to adjust its corresponding body of evidence to obtain the weighted averaging evidence before using Dempster’s combination rule.
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