Minimax Robust Decentralized Detection

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

doi:10.1109/18.272453

Minimax Robust Decentralized Detection

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

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	35-40
Number of pages	6
Journal	IEEE Transactions on Information Theory
Volume	40
Issue number	1
DOIs	https://doi.org/10.1109/18.272453
State	Published - Jan 1994

Keywords

Decentralized detection
least favorable distributions
minimax optimization
robust hypothesis testing

ASJC Scopus subject areas

Information Systems
Computer Science Applications
Library and Information Sciences

Online availability

10.1109/18.272453

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author = "Veeravalli, {Venugopal V.} and Tamer Basar and Poor, {H. Vincent}",

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KW - minimax optimization

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Minimax Robust Decentralized Detection

Abstract

Keywords

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