A family of adaptive filter algorithms with decorrelating properties

Author

Rupp, Markus

Author_Institution

Wireless Res. Lab., AT&T Bell Labs., Holmdel, NJ, USA

Volume

46

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

771

Lastpage

775

Abstract

Although the normalized least mean square (NLMS) algorithm is robust, it suffers from low convergence speed if driven by highly correlated input signals. One method presented to overcome this problem is the Ozeki/Umeda (1984) affine projection (AP) algorithm. The algorithm applies update directions that are orthogonal to the last P input vectors and thus allows decorrelation of an AR(P) input process, speeding up the convergence. This article presents a simple approach to show this property, which furthermore leads to the construction of new algorithms that can handle other kinds of correlations such as MA and ARMA processes. A statistical analysis is presented for this family of algorithms. Similar to the AP algorithm, these algorithms also suffer a possible increase in the noise energy caused by their pre-whitening filters

Keywords

adaptive filters; adaptive signal processing; autoregressive moving average processes; autoregressive processes; convergence of numerical methods; correlation methods; filtering theory; noise; recursive estimation; statistical analysis; AR process; ARMA process; NLMS algorithm; adaptive filter algorithms; affine projection algorithm; convergence speed; correlated input signals; decorrelating properties; input vectors; noise energy; normalized least mean square; pre-whitening filters; recursive algorithms; statistical analysis; update directions; Adaptive filters; Adaptive signal processing; Convergence; Decorrelation; Error analysis; IIR filters; Least squares approximation; Robustness; Signal processing algorithms; Speech processing;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.661344

Filename

661344