An Information Processing Approach to Distributed Detection

We apply the recent theory of information processing to a hybrid distributed detection architecture that combines the traditional parallel and tandem architectures. Central to this theory is the Kullback-Leibler discrimination distance and a quantity known as the information transfer ratio, defined as the ratio of the KL distances between the distributions characterizing the input and output of a system. We characterize the asymptotic performance of a proposed hybrid system and compare it with the performance of the parallel, tandem and centralized architectures. We conclude with an illustrative example.