A New Variable Step Size LMS Adaptive Filtering Algorithm and its Simulations

Abstract

. Aiming at the disadvantage of the variable step size LMS adaptive filtering algorithms' convergence speed contradicting its steady-state error, a novel non-liner functional relationship between μ (n) and error signal e (n) was established. On the basis of the functional relationship, a new algorithm of variable step size LMS adaptive filtering was presented. The step size factor of the new algorithm is adjusted by the absolute value of the product of the current and former errors. It also uses the absolute estimation error compensation terms disturbance to speed up the convergence of adaptive filter tap weight vector. At the same time, the algorithm considers the relationship between step length of the last iteration and the former M error signal. As a result the algorithm has higher convergence characteristic and small steady state error. The theoretical analysis and simulation results show that the new algorithm has faster convergence speed, lower steady state error and better performance of noise suppression, also show the overall performance of this algorithm exceeds some others condition.