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

73 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 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

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

Access to Document

10.1109/18.272453

Cite this

@article{ecbf8f2753e647adabbed29a3bf81e8f,

title = "Minimax Robust Decentralized Detection",

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.",

keywords = "Decentralized detection, least favorable distributions, minimax optimization, robust hypothesis testing",

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

note = "Funding Information: Manuscript received July 29, 1992; revised May 15, 1993. This work was supported in part by the Joint Services Electronics Program under Grant NOO 014-90-J-1270 through the University of Illinois. This paper was presented in part at the 26th Annual Conference on Information Sciences and Systems, Princeton, NJ, Mar. 1992.",

year = "1994",

month = jan,

doi = "10.1109/18.272453",

language = "English (US)",

volume = "40",

pages = "35--40",

journal = "IEEE Transactions on Information Theory",

issn = "0018-9448",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "1",

}

TY - JOUR

T1 - Minimax Robust Decentralized Detection

AU - Veeravalli, Venugopal V.

AU - Basar, Tamer

AU - Poor, H. Vincent

N1 - Funding Information: Manuscript received July 29, 1992; revised May 15, 1993. This work was supported in part by the Joint Services Electronics Program under Grant NOO 014-90-J-1270 through the University of Illinois. This paper was presented in part at the 26th Annual Conference on Information Sciences and Systems, Princeton, NJ, Mar. 1992.

PY - 1994/1

Y1 - 1994/1

N2 - 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.

AB - 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.

KW - Decentralized detection

KW - least favorable distributions

KW - minimax optimization

KW - robust hypothesis testing

UR - http://www.scopus.com/inward/record.url?scp=0028202724&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028202724&partnerID=8YFLogxK

U2 - 10.1109/18.272453

DO - 10.1109/18.272453

M3 - Article

AN - SCOPUS:0028202724

SN - 0018-9448

VL - 40

SP - 35

EP - 40

JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

IS - 1

ER -

Minimax Robust Decentralized Detection

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this