New optimal variable step size-adaptive regularized-affine projection algorithm

Abstract

In this paper, a new optimal variable step size-adaptive regularized-affine projection algorithm (OVSS-AR-APA) is proposed and compared with existing variable step size APA family of algorithms. Instead of having a constant regularization parameter, the modified sigmoid function variation is used to choose the optimum regularization parameter in the proposed algorithm. Here, the algorithm dynamically adjusts the regularization parameter according to input noise variations. Also, the exponentially weighted value of the error with variable regularization parameter is used for making the step size parameter variable. The existing and proposed algorithms are implemented for noise cancellation from audio signals at various input SNR levels for different filter orders. It is observed from simulations that the proposed algorithm outperforms the existing algorithms in terms of SNR improvements, MSE and convergence rate at all input SNR levels for different filter orders with moderate computational complexity. The OVSS-AR-APA algorithm shows a maximum of 21.54 dB of improvement in SNR at -20dB input SNR level at filter order 5.