Title :
On a class of computationally efficient, rapidly converging, generalized NLMS algorithms
Author :
Morgan, Dennis R. ; Kratzer, Steven G.
Author_Institution :
Dept. of Acoust. & Audio Commun. Res., Lucent Technol., Murray Hill, NJ, USA
Abstract :
Over the last decade, a certain computationally efficient, rapidly converging adaptive filtering algorithm has been independently discovered many times. The algorithm can be viewed as a generalization of the normalized LMS (NLMS) algorithm that updates on the basis of multiple input signal vectors. This article compares and discusses the different approaches to and embellishments of the basic algorithm, and contrasts the various interpretations from different perspectives.
Keywords :
adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; least mean squares methods; NLMS algorithm; adaptive filtering algorithm; computationally efficient algorithms; generalized NLMS algorithms; multiple input signal vectors; normalized LMS; rapidly converging algorithms; Adaptive filters; Convergence; Covariance matrix; Error correction; Filtering algorithms; Finite impulse response filter; History; Least squares approximation; Signal processing algorithms; Stochastic processes;
Journal_Title :
Signal Processing Letters, IEEE
