Abstract:

In order to improve the overall performance of variable stepsize least mean square (LMS) algorithms under the low signal-to-noise ratio (SNR) condition, a new variable stepsize LMS algorithm based on modified hyperbolic tangent function is presented. The anti-noise performance of this algorithm and the influence exerted by the introducing parameters on convergence, tracking, steady state error and misadjustment are discussed minutely in both theoretical analysis and simulation experiment. The theoretical analysis and simulation results show that this algorithm can achieve both better convergence speed, tracking speed，lower steadystate error and misadjustment under high and low SNR conditions. The results also show the overall performance of this algorithm exceeds some others under low SNR condition.