Maximum a posteriori maximum entropy order determination

Author

Knockaert, Luc

Author_Institution

Dept. of Inf. Technol., INTEC, Ghent, Belgium

Volume

45

Issue

6

fYear

1997

fDate

6/1/1997 12:00:00 AM

Firstpage

1553

Lastpage

1559

Abstract

An instance crucial to most problems in signal processing is the selection of the order of a presupposed model. Examples are the determination of the putative number of signals present in white Gaussian noise or the number of noise-contaminated sources impinging on a passive sensor array. It is shown that maximum a posteriori Bayesian arguments, coupled with maximum entropy considerations, offer an operational and consistent model order selection scheme, competitive with the minimum description length criterion

Keywords

Bayes methods; Gaussian noise; array signal processing; direction-of-arrival estimation; maximum entropy methods; maximum likelihood estimation; signal processing; white noise; maximum a posteriori Bayesian arguments; maximum a posteriori maximum entropy order determination; minimum description length criterion; model order selection; noise contaminated sources; passive sensor array; signal processing; white Gaussian noise; Array signal processing; Bayesian methods; Entropy; Gaussian noise; Integrated circuit modeling; Integrated circuit noise; Lagrangian functions; Maximum likelihood estimation; Sensor arrays; Sequential analysis;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.599997

Filename

599997