Efficient Computing of Dempster-Shafer Theoretic Conditionals for Big Hard/Soft Data Fusion

摘要

Ahstract- While hard sensor fusion is a highly developed discipline with vast methods, the inclusion of soft evidence continues to gain significant interest because soft sensors in the fusion process has certain merits, such as the ability to model attributes of interest (e.g., emotional level) that hard sensor may not. However, how to combine the hard/soft sensor data efficiently is a challenging problem, especially when the data set becomes large. In this study, a novel algorithm is proposed to apply the Conditional Core Theorem (CCT) in computing the Fagin-Halpern conditionals in the fusion of bodies of evidence with disparate frames of discernment. The computational complexity of the proposed algorithm is derived analytically and simulations are carried out to demonstrate the efficiency of the proposed algorithm.