Parallel implementation of analytic data fusion

Abstract

A description is given of an uncertainty and parallel data fusion approach that has been developed and tested. This fusion algorithm is based on the interaction of two constraints: the principle of knowledge source corroboration, which tends to maximize the final belief in a given proposition (often modeled by a probability density function or fuzzy membership distribution) if either of the knowledge sources supports the occurrence of the proposition; and the principle of belief enhancement/withdrawal which adjusts the belief of one knowledge source according to the belief of a second knowledge source by maximizing the similarity between the two source outputs. These two principles are combined by maximizing a positive linear combination of these two constraints related by a fusion function, to be determined. The implementation of this method was performed on an NCUBE hypercube parallel computer.<>