Variable Regularized Fast Affine Projections

This paper introduces a variable regularization method for the fast affine projection algorithm (VR-FAP). It is inspired by a recently introduced technique for variable regularization of the classical, affine projection algorithm (VR-APA). In both algorithms, the regularization parameter varies as a function of the excitation, measurement noise, and residual error energies. Because of the dependence on the last parameter, VR-APA and VR-FAP demonstrate the desirable property of fast convergence (via a small regularization value) when the convergence is poor and deep convergence/immunity to measurement noise (via a large regularization value) when the convergence is good. While the regularization parameter of APA is explicitly available for on-line modification, FAP's regularization is only set at initialization. To overcome this problem we use noise-injection with the noisepower proportional to the variable regularization parameter. As with their fixed regularization versions, VR-...

• Type: Conference
• Year: 2007
• Where: ICASSP
• Authors: Deepak Challa, Steven L. Grant, Asif Iqbal Mohammad
• Keywords: Affine Projection Algorithm, ICASSP 2007, Regularization Parameter, Signal Processing, Variable Regularization