An evidential reasoning approach to distributed multiple-hypothesis detection

A distributed detection system consisting of n local detectors and a fusion center is considered. The local detectors send their inferences with regard to a set of M candidate hypotheses to the fusion center where the final decision is made. Unlike conventional detectors which produce single-hypothesis hard decisions, each local detector produces a "subset" of the M hypotheses and a discrete confidence level associated with that subset. The fusion center then employs Dempster\´s combining rule for evidence aggregation. The optimum confidence-based partitioning of local decision space with respect to the system performance is studied. For the M=3 case, it is shown that the presented system greatly outperforms the one where each local detector provides a single-hypothesis hard decision.