Decentralized Detection'

Consider a set of sensors that receive observations from the environment and transmit finite-valued messages to a fusion center that makes a final decision on one out of M alternative hypotheses. The problem is to provide rules according to which the sensors should decide what to transmit, in order to optimize a measure of organizational performance. We overview the available theory for the Bayesian formulation, and improve upon the known results for the Neyman-Pearson variant of the problem. We also discuss (i) computational issues, (ii) asymptotic results, (iii) generalizations to more complex organizations, and (iv) sequential problems. To appear in Advances in Statistical Signal Processing, Vol. 2: Signal Detection, H. V. Poor and J. B. Thomas, Editors. 1. Research supported by the ONR under Contract N00014-84-K-0519 (NR 649-003) and by the ARO under contract DAAL03-86-K-0171. 2. Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.

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