Proportionate Adaptive Filtering for Block Sparse System Identification
Information Theory
2015-12-01 v2 math.IT
Abstract
In this paper, a new family of proportionate normalized least mean square (PNLMS) adaptive algorithms that improve the performance of identifying block-sparse systems is proposed. The main proposed algorithm, called block-sparse PNLMS (BS-PNLMS), is based on the optimization of a mixed l2,1 norm of the adaptive filter coefficients. It is demonstrated that both the NLMS and the traditional PNLMS are special cases of BS-PNLMS. Meanwhile, a block-sparse improved PNLMS (BS-IPNLMS) is also derived for both sparse and dispersive impulse responses. Simulation results demonstrate that the proposed BS-PNLMS and BS-IPNLMS algorithms outperformed the NLMS, PNLMS and IPNLMS algorithms with only a modest increase in computational complexity.
Keywords
Cite
@article{arxiv.1508.04172,
title = {Proportionate Adaptive Filtering for Block Sparse System Identification},
author = {Jianming Liu and Steven L. Grant},
journal= {arXiv preprint arXiv:1508.04172},
year = {2015}
}