An Enhanced IAF-PNLMS Adaptive Algorithm for Sparse Impulse Response Identification

• Published in: IEEE transactions on signal processing 2012-06, Vol.60 (6), p.3301-3307
• Main Authors: das Chagas de Souza, Francisco, Seara, R., Morgan, D. R.
• Format: Article
• Language: English
• Subjects: Adaptive filtering; Adaptive filters; Applied sciences; Classification algorithms; Convergence; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filtering algorithms; gain redistribution; Information, signal and communications theory; Numerical simulation; proportionate normalized least-mean-square (PNLMS) algorithm; Signal and communications theory; Signal processing algorithms; Signal, noise; sparse impulse response; system identification; Telecommunications and information theory; thresholding technique; Vectors
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2012.2190407

This correspondence presents an individual-activation-factor proportionate normalized least-mean-square (IAF-PNLMS) algorithm that (during the adaptive process) uses a new gain distribution strategy for updating the filter coefficients. This strategy consists of increasing the gain assigned to the inactive coefficients as the active ones approach convergence. For such, whenever a predefined threshold is crossed during the learning process, a new gain distribution is carried out, rather than to assign gains proportional to coefficient magnitudes as the IAF-PNLMS algorithm does. This new version of the IAF-PNLMS algorithm leads to a better distribution of the adaptation energy over the whole learning process. As a consequence, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming the IAF-PNLMS and other well-known PNLMS-type algorithms.