Theoretic analysis of the γ-LMS algorithm

Author

Wu, Wen-Rong ; Chen, Po-Cheng

Author_Institution

Dept. of Commun. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan

fYear

1994

fDate

5-8 Dec 1994

Firstpage

382

Lastpage

387

Abstract

The AR modeling is widely used in signal processing. The coefficients of AR model can be easily obtained by a LMS prediction error filter. However, it is known that such filter will give bias coefficients when the input signal is corrupted by noise. In previous works, Treicher [1979] suggested the γ-LMS algorithm to reduce the bias problem caused by Gaussian noise. This paper gives the theoretical analysis of the γ-LMS algorithm. We derive the close form solution of the second order statistics of the tap-weight vector. Computer simulations are provided to show the accuracy of our theoretical result

Keywords

Gaussian noise; autoregressive processes; higher order statistics; least mean squares methods; signal processing; γ-LMS algorithm; AR modeling; Gaussian noise; bias problem; close form solution; second order statistics; signal processing; tap-weight vector; Adaptive filters; Algorithm design and analysis; Gaussian noise; Least squares approximation; Mean square error methods; Predictive models; Size control; Stability; Statistics; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1994. APCCAS '94., 1994 IEEE Asia-Pacific Conference on

Conference_Location

Taipei

Print_ISBN

0-7803-2440-4

Type

conf

DOI

10.1109/APCCAS.1994.514580

Filename

514580