Non-Gaussian multivariate adaptive AR estimation using the super exponential algorithm

Author

Martone, Massimiliano

Author_Institution

Telecomm. Group, Watkins-Johnson Co., Gaithersburg, MD, USA

Volume

44

Issue

10

fYear

1996

fDate

10/1/1996 12:00:00 AM

Firstpage

2640

Lastpage

2644

Abstract

We formulate as a deconvolution problem the causal/noncausal non-Gaussian multichannel autoregressive (AR) parameter estimation problem. The super exponential algorithm presented in a paper by Shalvi and Weinstein (1993) is generalized to the vector case. We present an adaptive implementation that is very attractive since it is higher order statistics (HOS) based but does not present the high computational complexity of methods proposed up to now

Keywords

adaptive estimation; autoregressive processes; computational complexity; deconvolution; higher order statistics; iterative methods; vectors; adaptive implementation; computational complexity; deconvolution problem; higher order statistics; multichannel autoregressive parameter estimation; nonGaussian multivariate adaptive AR estimation; super exponential algorithm; vector case; Adaptive signal processing; Channel estimation; Deconvolution; Equations; Higher order statistics; Image analysis; Parameter estimation; Signal analysis; Signal processing algorithms; Time series analysis;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.539052

Filename

539052