A Fast Converging Algorithm for System with Highly Sparse Impulse Response

Author

Zhang, Yonggang ; Wang, Chengcheng ; Li, Ning

Author_Institution

Coll. of Autom., Harbin Eng. Univ., Harbin, China

fYear

2012

fDate

23-26 June 2012

Firstpage

831

Lastpage

834

Abstract

The proportionate normalized least-mean-square (PNLMS) algorithm with individual activation factors (IAFPNLMS) converges fast when the echo path is highly sparse, and has been used in system identification. Unfortunately, it suffers from slow convergence speed after the fast initial process. To solve the problem, in this paper, the idea of mu-law PNLMS (MPNLMS) algorithm is introduced into the IAFPNLMS algorithm, which results in IAF-MPNLMS algorithm. Simulations show that the proposed algorithm performs better than MPNLMS, improved PNLMS (IPNLMS) and IAFPNLMS algorithms for system identification with highly sparse impulse response.

Keywords

convergence; identification; least mean squares methods; transient response; echo path; fast converging algorithm; highly sparse impulse response; individual activation factors; mu-law PNLMS algorithm; proportionate normalized least-mean-square algorithm; system identification; Adaptation models; Algorithm design and analysis; Convergence; Educational institutions; Least squares approximation; Signal processing; Signal processing algorithms; Adaptive filters; IAF-PNLMS; MPNLMS; highly sparse impulse response; system identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4673-1365-0

Type

conf

DOI

10.1109/CSO.2012.187

Filename

6274851