Abstract
In view of the fact that Dempster-Shafer (D-S) evidence theory is unable to fuse data of multiple different kinds of sensors, an improved D-S evidence theory method based on the fusion of support and confidence entropy is proposed. Firstly, the identification framework of evidence theory is improved; secondly, Spearman correlation coefficient is introduced to represent the correlation between evidences; thirdly, a new confidence entropy is defined to describe the inconsistent uncertainty and non-specific uncertainty between evidences; then, the evidence set is modified by the combination of correlation and confidence entropy; finally, Dempster combination rule is used for information fusion. The simulation results confirm that the improved method of D-S evidence theory is feasible and more effective than the traditional algorithm.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call