Noise constrained LMS algorithm

Author

Wei, Yongbin ; Gelfand, Saul B. ; Krogmeier, James V.

Author_Institution

Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

3

fYear

1997

fDate

21-24 Apr 1997

Firstpage

2353

Abstract

In many identification and tracking problems, an accurate estimate of the measurement noise variance is available. A partially adaptive LMS-type algorithm is developed which can exploit this information while maintaining the simplicity and robustness of LMS. This noise constrained LMS (NCLMS) algorithm is a type of variable step-size LMS algorithm, which is derived by adding constraints to the mean-square error optimization. The convergence and steady-state performance are analyzed. Both the theoretical results and simulations show that NCLMS can dramatically outperform LMS, RLS and other variable step-size LMS algorithms in a sufficiently noisy environment

Keywords

Gaussian channels; adaptive filters; convergence of numerical methods; filtering theory; identification; least mean squares methods; noise; tracking filters; FIR AWGN channels; RLS; adaptive algorithms; adaptive filtering; convergence; identification problems; mean square error optimization; measurement noise variance estimation; noise constrained LMS algorithm; noisy environment; partially adaptive LMS type algorithm; simulations; steady-state performance; tracking problems; variable step size; Additive white noise; Convergence; Finite impulse response filter; Gaussian noise; Least squares approximation; Noise measurement; Noise robustness; Resonance light scattering; Steady-state; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.599525

Filename

599525