Line search algorithms for adaptive filtering

Author

Davila, Carlos E.

Author_Institution

Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

Volume

41

Issue

7

fYear

1993

fDate

7/1/1993 12:00:00 AM

Firstpage

2490

Lastpage

2494

Abstract

Line search algorithms for adaptive filtering that choose the convergence parameter so that the updated filter vector minimizes the sum of squared errors on a linear manifold are described. A shift invariant property of the sample covariance matrix is exploited to produce an adaptive filter stochastic line search algorithm for exponentially weighted adaptive equalization requiring 3N+5 multiplications and divisions per iteration. This algorithm is found to have better numerical stability than fast transversal filter algorithms for an application requiring steady-state tracking capability similar to that of least-mean square (LMS) algorithms. The algorithm is shown to have faster initial convergence than the LMS algorithm and a well-known variable step size algorithm having similar computational complexity in an adaptive equalization experiment

Keywords

adaptive filters; computational complexity; convergence of numerical methods; filtering and prediction theory; iterative methods; LMS algorithm; adaptive filtering; computational complexity; convergence parameter; exponentially weighted adaptive equalization; fast transversal filter algorithms; iteration; least-mean square; linear manifold; numerical stability; sample covariance matrix; shift invariant property; steady-state tracking capability; stochastic line search algorithm; variable step size algorithm; Adaptive equalizers; Adaptive filters; Convergence; Covariance matrix; Error correction; Filtering algorithms; Least squares approximation; Nonlinear filters; Stochastic processes; Vectors;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.224257

Filename

224257