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 norm of the adaptive filter's 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.
Recommended Citation
J. Liu and S. L. Grant, "Proportionate Adaptive Filtering for Block-sparse System Identification," IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 24, no. 4, pp. 623 - 630, Association for Computing Machinery (ACM); Institute of Electrical and Electronics Engineers, Apr 2016.
The definitive version is available at https://doi.org/10.1109/TASLP.2015.2499602
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Block-sparse; Proportionate adaptive algorithm; Sparse system identification
International Standard Serial Number (ISSN)
2329-9290
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery (ACM); Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Apr 2016
