Theories in distributed decision fusion: comparison and generalization
Stelios C. A. Thomopoulos · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
Distributed Decision (Evidence) Fusion (DD(E)F) exhibits some interesting characteristics which are not present in centralized, or raw data, fusion. The interesting characteristics relate to the semantic information that the decisions (in the broader sense of the term) convey which (semantic information) is not present, at least explicitly, when raw data is fused. Different theories and results related to DD(E)F have appeared in the literature. Each theory takes a different stand on the definition of how to measure evidence or combine decisions. The objective of this paper is to investigate the nature of DD(E)F and establish a comparative basis between the two most prominent theories in DD(E)F, namely the Bayesian and Dempster-Shafer theories. To that extent, the similarities and differences between the two theories that result from the semantic differences in the format of the fused information are investigated. A performance comparison between the two theories is attempted. A Generalized Evidence Processing (GEP) theory that extends the Bayesian approach into fuzzy decision making is used to compare the performance of a Bayesian soft decision making system with that of a hard decision making Bayesian system. The similarities and differences between the GEP combining rule and the Dempster's combining rule are discussed and a consistency comparison between the two rules is performed.