A computationally efficient approach to the design of decentralized Bayesian detection systems is presented. This procedure is based upon an alternate representation of the minimum average cost in terms of a modified form of the Kolmogorov variational distance. The utility of the approach is demonstrated by applying it to the design and performance evaluation of three decentralized detection structures. In all these structures, the design of the optimum systems reduces to the optimization of a single function of a certain number of variables. Numerical examples are presented for illustration.
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