On Decentralized Detection With Partial Information Sharing Among Sensors

We 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 c...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2011-04, Vol.59 (4), p.1759-1765
Main Authors: Kreidl, O P, Tsitsiklis, J N, Zoumpoulis, S I
Format: Article
Language:English
Subjects:
Applied sciences
Architecture
Asymptotic properties
Bayesian methods
Broadcasting
Decentralized
Decentralized detection
Detection, estimation, filtering, equalization, prediction
error exponent
Exact sciences and technology
Exponents
Information, signal and communications theory
Messages
nontree network
Optimization
Permissible error
Probabilistic logic
Random variables
Sensor fusion
sensor networks
Sensors
Signal and communications theory
Signal, noise
Telecommunications and information theory
Wireless sensor networks
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Summary:We 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, we allow a subset of the sensors to both transmit their messages to the fusion center and to also broadcast them to the remaining sensors. We focus on the following architectural question: Is there a significant performance improvement when we allow such a message broadcast? We consider the error exponent (asymptotically, in the limit of a large number of sensors) for the Neyman-Pearson formulation of the detection problem. We prove that the sharing of messages does not improve the optimal error exponent.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2010.2099223