Minimax Robust Decentralized Detection

Venugopal V. Veeravalli, Tamer Basar, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

Decentralized detection problems are studied where the sensor distributions are not specified completely. The sensor distributions are assumed to belong to known uncertainty classes. It is shown for a broad class of such problems that a set of least favorable distributions exists for minimax robust testing between the hypotheses. It is hence established that the corresponding minimax robust tests are solutions to simple decentralized detection problems for which the sensor distributions are specified to be the least favorable distributions.

Original languageEnglish (US)
Pages (from-to)35-40
Number of pages6
JournalIEEE Transactions on Information Theory
Volume40
Issue number1
DOIs
StatePublished - Jan 1994

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

Keywords

  • Decentralized detection
  • least favorable distributions
  • minimax optimization
  • robust hypothesis testing

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