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Technical Program

Paper Detail

Paper:	SPTM-L5.4
Session:	LMS-type Adaptive Filters
Time:	Wednesday, May 17, 15:00 - 15:20
Presentation:	Lecture
Topic:	Signal Processing Theory and Methods: Adaptive Systems and Filtering
Title:	On Convergence of Proportionate-Type NLMS Adaptive Algorithms
Authors:	Milos Doroslovacki, George Washington University, United States; Hongyang Deng, Acoustic Technologies, Inc., United States
Abstract:	We specify the general form of proportionate-type NLMS adaptive algorithms and show that for sufficiently small adaptation stepsize parameter, the algorithms can be exponentially stable, globally convergent and robust to unmodeled dynamics and measurement noise. Also, we show that for small adaptation stepsize parameter and stationary inputs, behavior of proportionate-type NLMS algorithms can be modeled by proportionate-type steepest descent algorithms. This motivates designing of proportionate-type NLMS adaptive algorithms by looking at the adjoint proportionate-type steepest descent algorithms.