A zero-attracting quaternion-valued least mean square algorithm for sparse system identification

Author

Mengdi Jiang ; Wei Liu ; Yi Li

Author_Institution

Dept. of Electron. & Electr. Eng., Univ. of Sheffield, Sheffield, UK

fYear

2014

fDate

23-25 July 2014

Firstpage

596

Lastpage

599

Abstract

Recently, quaternion-valued signal processing has received more and more attention. In this paper, the quaternion-valued sparse system identification problem is studied for the first time and a zero-attracting quaternion-valued least mean square (LMS) algorithm is derived by considering the l₁ norm of the quaternion-valued adaptive weight vector. By incorporating the sparsity information of the system into the update process, a faster convergence speed is achieved, as verified by simulation results.

Keywords

least mean squares methods; signal processing; LMS algorithm; quaternion valued adaptive weight vector; quaternion valued signal processing; quaternion valued sparse system identification problem; sparse system identification; sparsity information; zero attracting quaternion valued least mean square algorithm; Convergence; Cost function; Least squares approximations; Quaternions; Signal processing; Signal processing algorithms; Vectors; LMS algorithm; adaptive filtering; quaternion; sparsity; system identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014 9th International Symposium on

Conference_Location

Manchester

Type

conf

DOI

10.1109/CSNDSP.2014.6923898

Filename

6923898