Analysis of the Gradient-Descent Total Least-Squares Adaptive Filtering Algorithm

Author

Arablouei, Reza ; Werner, Stefan ; Dogancay, Kutluyil

Author_Institution

Inst. for Telecommun. Res., Univ. of South Australia, Mawson Lakes, SA, Australia

Volume

62

Issue

5

fYear

2014

fDate

1-Mar-14

Firstpage

1256

Lastpage

1264

Abstract

The gradient-descent total least-squares (GD-TLS) algorithm is a stochastic-gradient adaptive filtering algorithm that compensates for error in both input and output data. We study the local convergence of the GD-TLS algoritlun and find bounds for its step-size that ensure its stability. We also analyze the steady-state performance of the GD-TLS algorithm and calculate its steady-state mean-square deviation. Our steady-state analysis is inspired by the energy-conservation-based approach to the performance analysis of adaptive filters. The results predicted by the analysis show good agreement with the simulation experiments.

Keywords

adaptive filters; least squares approximations; stochastic processes; energy-conservation; gradient-descent total least-squares algorithm; steady-state analysis; steady-state mean-square deviation; stochastic-gradient adaptive filtering algorithm; Adaptive filters; Algorithm design and analysis; Signal processing algorithms; Stability criteria; Steady-state; Vectors; Adaptive filtering; Rayleigh quotient; mean-square deviation; performance analysis; stability; total least-squares;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2014.2301135

Filename

6716043