Fundamental structures and asymptotic performance criteria in decentralized binary hypothesis testing

Author

Delic, Hakan ; Papantoni-Kazakos, P. ; Kazakos, Demetrios

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Southwestern Louisiana, Lafayette, LA, USA

Volume

43

Issue

1

fYear

1995

fDate

1/1/1995 12:00:00 AM

Firstpage

32

Lastpage

43

Abstract

Two fundamental distributed decision network structures are considered: the first system consists of finite number of sensors, each collecting asymptotically many data, while the second one employs asymptotically many sensors, each collecting a single datum. For binary hypothesis testing, the Neyman-Pearson criterion is utilized and justified via information theoretic arguments. An asymptotic relative efficiency performance measure is used to establish tradeoffs between the two structures, by comparing the performance characteristics of the decentralized detection systems to their centralized counterparts

Keywords

distributed decision making; information theory; sensor fusion; signal detection; Neyman-Pearson criterion; asymptotic performance criteria; asymptotic relative efficiency performance measure; decentralized binary hypothesis testing; decentralized detection systems; distributed decision network structures; information theoretic arguments; Computer networks; Decision making; Helium; Information theory; Infrared detectors; Infrared sensors; Intelligent networks; Sensor phenomena and characterization; Sensor systems; System testing;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/26.385944

Filename

385944