Adaptive algorithms for constrained ARMA signals in the presence of noise

Author

Nehorai, Arye ; Stoica, Peter

Author_Institution

Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA

Volume

36

Issue

8

fYear

1988

fDate

8/1/1988 12:00:00 AM

Firstpage

1282

Lastpage

1291

Abstract

A family of algorithms is developed for adaptive parameter estimation of constrained autoregressive moving-average (ARMA) signals in the presence of noise. These algorithms utilize a priori information about the signal´s properties, such as its spectral type (for example, low-pass, bandpass, etc.) or a spatial-domain characteristic. Special applications include modeling of autoregressions (AR) and signals of known spectral type in the presence of noise, signal deconvolution, image deblurring and multipath parameter estimation. Selected results of simulations are included to demonstrate the performance of the algorithms

Keywords

noise; parameter estimation; signal processing; a priori information; adaptive algorithms; adaptive parameter estimation; autoregressive moving average signals; constrained ARMA signals; image deblurring; multipath parameter estimation; noise; signal deconvolution; spatial-domain characteristic; spectral type; Adaptive algorithm; Deconvolution; Image restoration; Integrated circuit noise; Least squares methods; Newton method; Noise measurement; Pollution measurement; Recursive estimation; White noise;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.1656

Filename

1656