Nonparametric decentralized sequential detection

Author

Kuh, Anthony

Author_Institution

Dept. of Electr. Eng., Hawaii Univ., Honolulu, HI, USA

fYear

1997

fDate

29 Jun-4 Jul 1997

Firstpage

528

Abstract

We consider a decentralized sequential detection problem with a set of sensors and a fusion center. Each sensor receives information from inputs and possibly other sensors at discrete times and transmits summary information to a fusion center which processes the summary information by performing a sequential test to make a decision on one of two hypotheses. The work discussed differs from previous work by Veervalli, Basar and Poor (1993) in that the conditional densities given each hypothesis are unknown. Information about making good decisions is learned from observing real data and employing reinforcement learning procedures

Keywords

feedforward neural nets; learning (artificial intelligence); sensor fusion; sequences; signal detection; conditional densities; feedforward neural network; fusion center; nonparametric decentralized sequential detection; real data observations; reinforcement learning procedures; sensors; sequential test; summary information transmission; Bayesian methods; Cost function; Dynamic programming; Learning; Neural networks; Performance evaluation; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory. 1997. Proceedings., 1997 IEEE International Symposium on

Conference_Location

Ulm

Print_ISBN

0-7803-3956-8

Type

conf

DOI

10.1109/ISIT.1997.613465

Filename

613465