The distributed detection problem is considered from an information-theoretic point of view. An entropy-based cost function is used for system optimization. This cost function maximizes the amount of information transfer between the input and the output. Distributed detection system topologies with and without a fusion center are considered, and an optimal fusion rule and optimal decision rules are derived.>

The signal detection problem is considered for the case in which distributed sensors are used and a global decision is desired. Local decisions from the sensors are fed to a data fusion center, which yields a global decision based on a fusion rule. A Bayesian formulation of the problem is considered, and a person-by-person optimization of the overall system is carried out. The special case of identical detectors with independent observations is considered as well. An illustrative example is presented.>