Fusion of likelihood ratios in distributed Bayesian detection

Author

Delic, Hakan ; Kazakos, Dimitri

Author_Institution

Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA

fYear

1991

fDate

13-16 Oct 1991

Firstpage

755

Abstract

A discrete-time Bayesian detection model is considered in which sensors collect data records, and it is assumed that any two sensors are statistically independent. A binary hypothesis is tested, and it is assumed that a state-variable random process model represents the two hypotheses for each sensor. The optimum Bayesian detector is constructed by the computation of the local likelihood ratios at each sensor in real time and the subsequent transmission of these ratios to the fusion center. A study is made of the performance for various parameters; in particular, the effects of the data size and the number of sensors on the performance of the detection scheme

Keywords

Bayes methods; probability; signal detection; signal processing; distributed Bayesian detection; likelihood ratios fusion; sensor fusion; signal detection; signal processing; state-variable random process model; Bayesian methods; Costs; Decision making; Detectors; Random processes; Sensor fusion; Sensor systems; Signal detection; Steady-state; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on

Conference_Location

Charlottesville, VA

Print_ISBN

0-7803-0233-8

Type

conf

DOI

10.1109/ICSMC.1991.169777

Filename

169777