Stochastic mean-square performance analysis of an adaptive Hammerstein filter

Author

Jeraj, J. ; Mathews, V.J.

Author_Institution

Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA

Volume

2

fYear

2004

fDate

17-21 May 2004

Abstract

This paper presents an almost sure (a.s.) mean-square performance analysis of an adaptive Hammerstein filter for the case when the measurement noise in the desired response signal is a martingale difference sequence. The system model consists of a series connection of a memoryless nonlinearity followed by a recursive linear filter. It is shown under the conditions of the analysis that the long-term time average of the squared excess estimation error of the adaptive filter can be made arbitrarily close to zero.

Keywords

IIR filters; adaptive filters; convergence; memoryless systems; nonlinear filters; recursive filters; stochastic processes; IIR filter; adaptive Hammerstein filter; convergence; estimation error long-term time average; martingale difference sequence; memoryless nonlinearity; recursive linear filter; recursive nonlinear adaptive filter; response signal measurement noise; squared excess estimation error; stochastic mean-square performance analysis; Adaptive filters; Cities and towns; Electric variables measurement; Equations; Noise measurement; Nonlinear filters; Nonlinear systems; Performance analysis; Polynomials; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-8484-9

Type

conf

DOI

10.1109/ICASSP.2004.1326360

Filename

1326360