Mean Square Performance of a family of Adaptive Algorithms for colored noise

Abstract

In real-time applications the characteristics and properties of a signal vary inconsistently. So, to maintain the integrity of such signals there is a need for effective adaptive filters. The conventional Least Mean Squared(LMS) algorithm is widely used because of its computational simplicity and ease of implementation. But, its convergence speed rapidly reduces when colored noise is present in the signal. Affine projection(AP) algorithms are used to speed up the convergence but have high computational costs. In this paper, the mean square performance of LMS and AP algorithms is analyzed when subject to white noise and colored noise.