Properties of the kurtosis performance surface in linear estimation: application to adaptive filtering

Author

Hübscher, Pedro I. ; Bermudez, José C M

Author_Institution

Nat. Inst. for Space Res., INPE, Sao Jose Dos Campos, Brazil

Volume

2

fYear

2004

fDate

17-21 May 2004

Abstract

The paper presents an analysis of the kurtosis performance surface as applied to linear estimation. The analysis concentrates on a modified kurtosis (MK) function used in implementations of the least mean kurtosis (LMK) adaptive algorithm. The MK function is shown to make the LMK algorithm applicable even for Gaussian inputs. The minimum of the MK function is derived and shown to be unique and to correspond to the Wiener solution of the mean square error (MSE) estimation problem. A quantitative comparison of the MSE and MK functions explains why the LMK adaptive algorithm is faster than MSE-based algorithms during the initial learning phase, becoming slower as it approaches steady-state.

Keywords

adaptive filters; mean square error methods; parameter estimation; statistical analysis; Gaussian inputs; MSE estimation; Wiener solution; adaptive filtering; kurtosis performance surface; least mean kurtosis adaptive algorithm; linear estimation; mean square error estimation; modified kurtosis function; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Cost function; Estimation error; Least squares approximation; Mean square error methods; Performance analysis; Signal processing algorithms; Steady-state;

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.2004.1326388

Filename

1326388