Information Bounds for Decentralized Sequential Detection

Author

Mei, Yajun

Author_Institution

Sch. of Ind. & Syst. Eng., Georgia Inst. of Technol., Atlanta, GA

fYear

2006

fDate

9-14 July 2006

Firstpage

2647

Lastpage

2651

Abstract

The main purpose of this paper is to develop an asymptotic theory for the decentralized sequential hypothesis testing problems under the frequentist framework. Sharp asymptotic bounds on the average sample numbers or sample sizes of sequential or fixed-sample tests are provided in the decentralized decision systems in different scenarios subject to error probabilities constraints. Asymptotically optimal tests are offered in the system with full local memory. Optimal binary quantizers are also studied in the case of additive Gaussian sensor noises

Keywords

Gaussian noise; decision making; error statistics; heuristic programming; sensor fusion; statistical testing; additive Gaussian sensor noises; asymptotic theory; decentralized decision systems; decentralized sequential detection; error probabilities constraints; information bounds; optimal binary quantizers; Additive noise; Bayesian methods; Electronic mail; Error probability; Feedback; Sensor fusion; Sensor systems; Sequential analysis; System testing; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 2006 IEEE International Symposium on

Conference_Location

Seattle, WA

Print_ISBN

1-4244-0505-X

Electronic_ISBN

1-4244-0504-1

Type

conf

DOI

10.1109/ISIT.2006.262133

Filename

4036452