Decentralized Multihypothesis Sequential Detection

被引：0

作者：

Wang, Yan ^{[1
]}

Mei, Yajun ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA 30332 USA

来源：

2010 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY | 2010年

关键词：

PROBABILITY RATIO TESTS; ASYMPTOTIC OPTIMALITY; DISTRIBUTED DETECTION; MULTIPLE SENSORS; NETWORKS; DESIGN; FUSION;

D O I：

10.1109/ISIT.2010.5513609

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This article is concerned with decentralized sequential testing of multiple hypotheses. In a sensor network system with limited local memory, raw observations are observed at the local sensors, and quantized into binary sensor messages that are sent to a fusion center, which makes a final decision. It is assumed that the raw sensor observations are distributed according to a set of M >= 2 specified distributions, and the fusion center has to utilize quantized sensor messages to decide which one is the true distribution. Asymptotically Bayes tests are offered for decentralized multihypothesis sequential detection by combining three existing methodologies together: tandem quantizers, unambiguous likelihood quantizers, and randomized quantizers.

引用

页码：1393 / 1397

页数：5

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