Multi-source information integration in intelligent systems using the plausibility measure

Abstract

Dempster-Shafer theory of evidence is particularly well suited for the aggregation and integration of information, however, a major disadvantage of this theory is that its time complexity increases geometrically as the number of evidential sources increases, In the paper, we develop a new multisource information fusion scheme using the plausibility measure. The method avoids using Dempster's rule of combination, in order to overcome the intractability of Dempster-Shafer computations, allowing the theory to be feasible in many more applications. A simple robotic vision system with object recognition data from multisensor is presented to highlight benefits of the new method.<>