Large Deviations Analysis to the Performance of Distributed Detection

Po-Ning Chen

ISBN 10: 3843369992 / ISBN 13: 9783843369992

Published by LAP LAMBERT Academic Publishing Okt 2010, 2010

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.

Seller Inventory # 9783843369992

Bibliographic Details

• Title: Large Deviations Analysis to the Performance of Distributed Detection
• Publisher: LAP LAMBERT Academic Publishing Okt 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 3843369992
• ISBN 13: 9783843369992
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 215 grams

About this title

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.

About the Author

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.