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An algorithm for determining the decision thresholds in a distributed detection problem

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3 Author(s)
Z. -B. Tang ; Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA ; K. R. Pattipati ; D. L. Kleinman

A decentralized binary hypothesis-testing problem is considered in which a number of subordinate decision makers (DMs) transmit their opinions, based on their own data, to a primary decision maker who, in turn, combines the opinions with his own data to make the final team decision. The necessary conditions for the person-by-person optimal decision rules of the DMs are derived. A nonlinear Gauss-Seidel iterative algorithm is developed to solve for the decision thresholds of a person-by-person optimal strategy. The algorithm is illustrated with several examples, and implications for distributed organizational design are pointed out

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:21 ,  Issue: 1 )