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Assistant Professor of Decision Sciences
Kreidl O. P., Tsitsiklis J. N., Zoumpoulis S. (2011). On Decentralized Detection With Partial Information Sharing Among Sensors IEEE Transactions on Signal Processing, 59(4), pp. 1759-1765.
The authors study a decentralized detection architecture in which each of a set of sensors transmits a highly compressed summary of its observations (a binary message) to a fusion center, which then decides on one of two alternative hypotheses.In contrast to the star (or “parallel”) architecture considered in most of the literature, the authors allow a subset of the sensors to both transmit their messages to the fusion center and to also broadcast them to the remaining sensors.The authors focus on the following architectural question: Is there a significant performance improvement when we allow such a message broadcast? The authors consider the error exponent (asymptotically, in the limit of a large number of sensors) for the Neyman-Pearson formulation of the detection problem.The authors prove that the sharing of messages does not improve the optimal error exponent.