A belief logarithmic similarity measure based on Dempster-Shafer theory and its application in multi-source data fusion

Abstract

Dempster-Shafer theory (DST) has attracted widespread attention in many domains owing to its powerful advantages in managing uncertain and imprecise information. Nevertheless, counterintuitive results may be generated once Dempster’s rule faces highly conflicting pieces of evidence. In order to handle this flaw, a new belief logarithmic similarity measure ( BLSM ) based on DST is proposed in this paper. Moreover, we further present an enhanced belief logarithmic similarity measure ( EBLSM ) to consider the internal discrepancy of subsets. In parallel, we prove that EBLSM satisfies several desirable properties, like bounded, symmetry and non-degeneracy. Finally, a new multi-source data fusion method based on EBLSM is well devised. Through its best performance in two application cases, specifically those pertaining to fault diagnosis and target recognition respectively, the rationality and effectiveness of the proposed method is sufficiently displayed.