A Zero-attracting Quaternion-valued Least Mean Square Algorithm for Sparse System Identification
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 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
Cite
@article{arxiv.1406.5721,
title = {A Zero-attracting Quaternion-valued Least Mean Square Algorithm for Sparse System Identification},
author = {Mengdi Jiang and Wei Liu and Yi Li},
journal= {arXiv preprint arXiv:1406.5721},
year = {2014}
}
Comments
This work is partially funded by National Grid, UK and will appear in the Proc. of the 9th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Manchester, UK, July 2014 (submitted in March 2014 and accepted on 18 April 2014)