Performance Analysis of Adaptive Algorithms for Noise Cancellation

Adaptive filters are, by design, time-variant and nonlinear systems that adapt to variations in signal statistics and that learn from their interactions with the environment. The success of their learning mechanism can be measured in terms of how fast they adapt to changes in the signal characteristics and how well they can learn given sufficient time. The main requirements and the performance measures for adaptive filters are the convergence speed and the asymptotic error. In this paper we focused on the analysis and performance comparison between two methods of implementing adaptive filtering algorithms, namely the Least Mean Squares (LMS) algorithm and the Multi split LMS (MSLMS) algorithm. The simulation results enable us to measure the performance of filter and show the convergence speed improvement when using MS LMS algorithms over the LMS algorithm.