A survey: Optimization and applications of evidence fusion algorithm based on Dempster–Shafer theory

Abstract

Since Dempster–Shafer evidence theory was proposed, it has been widely and successfully used in many fields including risk analysis, fault diagnosis, wireless sensor networks, health prognosis, image processing, and target tracking, etc. However, many counter-intuitive results of data fusion will be obtained when evidence fused is highly conflicting. So far, this is still an open issue. To address this issue, many methods have been proposed, but they have not been comprehensively summarized in recent years. In this paper, a detailed survey is set forth about the optimization and application of evidence fusion algorithms based on Dempster–Shafer theory. Firstly, the principle of Dempster–Shafer evidence theory is introduced comprehensively. Then, the existing researches on modifying combination rule and pre-processed pieces of evidence to solve the counter-intuitive problem are reviewed in detail. Next, the performance of these studies is demonstrated, deeply analyzed, and discussed through experiments on general examples. And finally, the application of Dempster–Shafer evidence theory in different fields is critically summarized. What is more, analysis of the current status and the development trend of the research on evidence theory are concluded, which can provide a more comprehensive understanding of the development of the Dempster–Shafer evidence theory.