Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient
AbstractIn order to meet the higher accuracy and system reliability requirements, the information fusion for multi-sensor systems is an increasing concern. Dempster–Shafer evidence theory (D–S theory) has been investigated for many applications in multi-sensor information fusion due to its flexibility in uncertainty modeling. However, classical evidence theory assumes that the evidence is independent of each other, which is often unrealistic. Ignoring the relationship between the evidence may lead to unreasonable fusion results, and even lead to wrong decisions. This assumption severely prevents D–S evidence theory from practical application and further development. In this paper, an innovative evidence fusion model to deal with dependent evidence based on rank correlation coefficient is proposed. The model first uses rank correlation coefficient to measure the dependence degree between different evidence. Then, total discount coefficient is obtained based on the dependence degree, which also considers the impact of the reliability of evidence. Finally, the discount evidence fusion model is presented. An example is illustrated to show the use and effectiveness of the proposed method. View Full-Text
Share & Cite This Article
Shi, F.; Su, X.; Qian, H.; Yang, N.; Han, W. Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient. Sensors 2017, 17, 2362.
Shi F, Su X, Qian H, Yang N, Han W. Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient. Sensors. 2017; 17(10):2362.Chicago/Turabian Style
Shi, Fengjian; Su, Xiaoyan; Qian, Hong; Yang, Ning; Han, Wenhua. 2017. "Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient." Sensors 17, no. 10: 2362.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.