Bayesian array signal processing in additive generalized Gaussian noise

Author

Kannan, B.

Author_Institution

Centre for Wireless Commun., Nat. Univ. of Singapore, Singapore

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

86

Lastpage

89

Abstract

We present a Bayesian approach for DOA and frequency estimation of narrow band signals in additive generalized Gaussian noise. Using Bayesian techniques, the posterior probability densities for DOA (direction of arrival) and frequency parameters are derived from the signal and noise models. These posterior probabilities are then used in the Metropolis-Hastings (M-H) algorithm to derive the samples for the DOA and frequency parameters. The performances of our algorithms are studied by plotting the MSEs (mean square errors) of the parameters for various SNRs. The MSEs of the parameters are compared with the CRLBs (Cramer Rao lower bound) for the generalized Gaussian models

Keywords

Bayes methods; Gaussian noise; array signal processing; direction-of-arrival estimation; frequency estimation; mean square error methods; probability; Bayesian array signal processing; Cramer Rao lower bound; DOA estimation; DOA parameters; MSE; Metropolis-Hastings algorithm; SNR; additive generalized Gaussian noise; algorithm performance; direction of arrival estimation; frequency parameters; mean square errors; narrow band signals; noise model; nonGaussian signal processing; posterior probability density; signal model; Acoustic noise; Additive noise; Array signal processing; Atmospheric modeling; Bayesian methods; Direction of arrival estimation; Frequency estimation; Gaussian noise; Sensor arrays; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955228

Filename

955228