Uncertain information fusion using belief measure and its application to signal classification
Jung-Jae Chao, Kuo-Chih Shao, Lain-Wen Jang · 2002
Dempster-Shafer theory provides a method for information fusion where uncertain elements exist. The degree of belief based on distinct bodies of evidence can be combined to form a new degree of belief which are appropriate on the basis of combined information. Thus, we can process each piece of information independently and then combine those available information for final inference. However, the computational complexity is a major problem in using Dempster's combining rule directly. In this paper, we consider the consonant information only and then derive formulas for combining rules which make the fusion procedure more systematic and easier than the Dempster's approach. To examine the performance, we study the signal classification problems involving two sensors and multiple hypotheses. As a result, it shows that the proposed system greatly outperforms the one without information fusion.