Title :
A Generalization of the Normalized LMS Algorithm
Author_Institution :
General Electric Company Building 4, Room 5 Court St. Plant Syracuse, NY 13221
fDate :
7/1/1987 12:00:00 AM
Abstract :
As previously shown, the normalized least mean square (NLMS)algorithm has superior convergence properties than the least meansquare(LMS) algorithm. However, the weight noise effect of the NLMS algorithm is large so that the steady state residue power islarger than that for the LMS algorithm. A generalized NLMSalgorithm is developed based upon the pseudoinverse of anestimated covariance matrix. A preliminary evaluation indicatesimproved performance can be attained but the implementationcomplexity might be high.
Keywords :
Convergence; Covariance matrix; Geometry; Interference cancellation; Least squares approximation; Radar antennas; Random variables; Signal processing algorithms; Steady-state; Working environment noise;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.1987.310892
