Bias-remedy least mean square equation error algorithm for IIR parameter recursive estimation

Author

Lin, Ji-Nan ; Unbehauen, Rolf

Author_Institution

Lehrstuhl fur Allgemeine und Theor. Elektrotechnik, Erlangen-Nurnberg Univ., Germany

Volume

40

Issue

1

fYear

1992

fDate

1/1/1992 12:00:00 AM

Firstpage

62

Lastpage

69

Abstract

In the area of infinite impulse response (IIR) system identification and adaptive filtering the equation error algorithms used for recursive estimation of the plant parameters are well known for their good convergence properties. However, these algorithms give biased parameter estimates in the presence of measurement noise. A new algorithm is proposed on the basis of the least mean square equation error (LMSEE) algorithm, which manages to remedy the bias while retaining the parameter stability. The so-called bias-remedy least mean square equation error (BRLE) algorithm has a simple form. The compatibility of the concept of bias remedy with the stability requirement for the convergence procedure is supported by a practically meaningful theorem. The behavior of the BRLE has been examined extensively in a series of computer simulations

Keywords

adaptive filters; digital filters; filtering and prediction theory; least squares approximations; parameter estimation; IIR parameter recursive estimation; adaptive filtering; bias remedy LMS equation error; computer simulations; convergence; infinite impulse response; least mean square equation error; measurement noise; parameter estimates; parameter stability; system identification; Adaptive filters; Convergence; Equations; Filtering algorithms; IIR filters; Noise measurement; Parameter estimation; Recursive estimation; Stability; System identification;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.157182

Filename

157182