Decentralized multihypothesis sequential detection

Yan Wang, Yajun Mei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2010 IEEE International Symposium on Information Theory, ISIT 2010 - Proceedings
Pages1393-1397
Number of pages5
DOIs
StatePublished - 2010
Event2010 IEEE International Symposium on Information Theory, ISIT 2010 - Austin, TX, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8103

Other

Other2010 IEEE International Symposium on Information Theory, ISIT 2010
Country/TerritoryUnited States
CityAustin, TX
Period6/13/106/18/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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