Large Deviations Analysis to the Performance of Distributed Detection: Neyman-Pearson and Bayes errors - Couverture souple

Chen, Po-Ning

 
9783843369992: Large Deviations Analysis to the Performance of Distributed Detection: Neyman-Pearson and Bayes errors
Couverture souple
ISBN 10 : 3843369992 ISBN 13 : 9783843369992
Editeur : LAP LAMBERT Academic Publishing, 2010

Synopsis

This book studies the performance of distributed detection systems by means of large deviation techniques under two distinct models. In the first model, the error performance is investigated as the number of sensors tends to infinity by assuming that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. It is found that when the second moment of the post-quantization log-likelihood ratio is unbounded, the Neyman-Pearson error exponent becomes a function of the test level; whereas the Bayes error exponent remains unaffected. Also shown is that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents but extends to the actual Bayes errors up to a multiplicative constant. In the second model, the null and alternative distributions become spatially correlated Gaussian, differing in the mean. The issue considered includes whether contiguous marginal likelihood ratio quantizers are optimal. It is shown that this is not true in general, and a sufficient condition is obtained under the case of a single observation per sensor.

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Présentation de l'éditeur

This book studies the performance of distributed detection systems by means of large deviation techniques under two distinct models. In the first model, the error performance is investigated as the number of sensors tends to infinity by assuming that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. It is found that when the second moment of the post-quantization log-likelihood ratio is unbounded, the Neyman-Pearson error exponent becomes a function of the test level; whereas the Bayes error exponent remains unaffected. Also shown is that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents but extends to the actual Bayes errors up to a multiplicative constant. In the second model, the null and alternative distributions become spatially correlated Gaussian, differing in the mean. The issue considered includes whether contiguous marginal likelihood ratio quantizers are optimal. It is shown that this is not true in general, and a sufficient condition is obtained under the case of a single observation per sensor.

Biographie de l'auteur

Po-Ning Chen received his Ph.D. from University of Maryland, College Park, U.S.A. in 1994. He jointed the National Chiao-Tung University, Taiwan as an associate professor in 1996 and later became a full professor in 2001. His research areas include information and coding theory, large deviation theory, distributed detection and sensor networks.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3843369992
ISBN 13 
9783843369992
Reliure
Broché
Nombre de pages
132
Coordonnées du fabricant
non disponible
Personne responsable
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