Analysis of LMS-Newton adaptive filtering algorithms with variable convergence factor

Author

Diniz, Paulo S R ; De Campos, Marcello L R ; Antoniou, Andreas

Author_Institution

Dept. of Electron., Federal Univ. of Rio de Janeiro, Brazil

Volume

43

Issue

3

fYear

1995

fDate

3/1/1995 12:00:00 AM

Firstpage

617

Lastpage

627

Abstract

An analysis of two LMS-Newton adaptive filtering algorithms with variable convergence factor is presented. The relations of these algorithms with the conventional recursive least-squares algorithm are first addressed. Their performance in stationary and nonstationary environments is then studied and closed-form formulas for the excess mean-square error (MSE) are derived. The paper deals, in addition, with the effects of roundoff errors for the case of fixed-point arithmetic. Specifically, closed-form formulas for the excess MSE caused by quantization are obtained. The paper concludes with experimental results that demonstrate the validity of the analysis presented

Keywords

Newton method; adaptive filters; adaptive signal processing; convergence of numerical methods; digital arithmetic; filtering theory; least mean squares methods; quantisation (signal); roundoff errors; LMS-Newton adaptive filtering algorithms; MSE; closed-form formulas; experimental results; fixed-point arithmetic; mean-square error; nonstationary environments; performance; quantization; recursive least-squares algorithm; roundoff errors; stationary environments; variable convergence factor; Adaptive filters; Algorithm design and analysis; Convergence; Filtering algorithms; Fixed-point arithmetic; Iterative algorithms; Least squares approximation; Resonance light scattering; Signal processing algorithms; Statistics;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.370617

Filename

370617