Performance comparison of adaptive filter algorithms for noise cancellation

The purpose of adaptive filter is to solve the dilemma of fast convergence rate and low mean square error. In the past two decades many adaptive filter algorithms have been presented and have claimed that they have good convergence speed and tracking properties. This paper describes the concept of adaptive noise cancellation, an alternative method of estimating signals corrupted by additive noise or interference. This paper summarizes three promising algorithms and gives a performance comparision via extensive simulation. Step size is the main parameter for the convergence rate and mean square error. The simulation analysis showed that RLS algorithm had faster convergence speed and smaller steady state error compared with basic LMS algorithm.The results shown, for convergence rate with less no of taps is the most difficult phase in signal processing applications for the perfect working of any system.