A PNLMS Algorithm With Individual Activation Factors

This paper presents a proportionate normalized least-mean-square (PNLMS) algorithm using individual activation factors for each adaptive filter coefficient, instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of th...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2010-04, Vol.58 (4), p.2036-2047
Main Authors: das Chagas de Souza, F., Tobias, O.J., Seara, R., Morgan, D.R.
Format: Article
Language:English
Subjects:
Acoustic applications
Activation
Adaptation
Adaptive filtering
Adaptive filters
Algorithms
Applied sciences
Chemical processes
Coefficients
Convergence
Detection, estimation, filtering, equalization, prediction
Encoding
Energy distribution
Exact sciences and technology
Filtering algorithms
Information, signal and communications theory
Miscellaneous
Packet switching
Propagation delay
proportionate normalized least-mean-square (PNLMS) algorithm
Signal and communications theory
Signal processing
Signal processing algorithms
Signal, noise
sparse impulse response
sparse system identification
System identification
Telecommunications and information theory
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Description
Summary:This paper presents a proportionate normalized least-mean-square (PNLMS) algorithm using individual activation factors for each adaptive filter coefficient, instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding adaptive filter coefficients, are recursively updated. This approach leads to a better distribution of the adaptation energy over the filter coefficients than the standard PNLMS does. Thereby, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming both the PNLMS and improved PNLMS (IPNLMS) algorithms.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2009.2038420