Kernelized set-membership approach to nonlinear adaptive filtering

Author

Malipatil, Amaresh V. ; Huang, Yih-Fang ; Andra, Srinivas ; Bennett, Kristin

Author_Institution

Dept. of Electr. Eng., Notre Dame Univ., IN, USA

Volume

4

fYear

2005

Abstract

In linear filtering, the set-membership normalized least mean squares (SM-NLMS) algorithm has been shown to exhibit desirable features of selective update and optimized variable step size. In this paper, a kernel approach to the SM-NLMS algorithm is presented that makes it feasible to address nonlinear problems. An online greedy approximation technique to achieve sparsity is discussed. Simulation results are presented for two practical problems: equalization of nonlinear inter-symbol interference (ISI) channels and predistortion of nonlinear high power amplifiers (HPA).

Keywords

adaptive equalisers; adaptive filters; intersymbol interference; least mean squares methods; linearisation techniques; nonlinear distortion; nonlinear filters; pattern classification; power amplifiers; HPA predistortion; ISI; SM-NLMS; kernelized set-membership filtering; linear regression; nonlinear adaptive filtering; nonlinear distortion; nonlinear high power amplifiers; nonlinear intersymbol interference equalization; nonlinearly separable pattern classification; online greedy approximation technique; set-membership normalized least mean squares algorithm; sparsity; supervised learning algorithm; Adaptive filters; Filtering algorithms; Interference; Kernel; Least squares approximation; Maximum likelihood detection; Nonlinear distortion; Samarium; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-8874-7

Type

conf

DOI

10.1109/ICASSP.2005.1415967

Filename

1415967