Proportionate Adaptive Filtering for Block Sparse System Identification

Jianming Liu; Steven L. Grant

doi:10.1109/taslp.2015.2499602

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arXiv · 1508.04172

Proportionate Adaptive Filtering for Block Sparse System Identification

Jianming Liu·Steven L. Grant

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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.

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BibTeX RIS

Jianming Liu, Steven L. Grant. 2015-11-29. Proportionate Adaptive Filtering for Block Sparse System Identification. https://doi.org/10.1109/taslp.2015.2499602

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