Sign normalised spline adaptive filtering algorithms against impulsive noise

In this paper, a sign normalised least mean square algorithm (SNLMS) based on Wiener spline adaptive filter, called SAF-SNLMS, is proposed. The proposed algorithm is derived by minimising the absolute value of the a posteriori error. Moreover, to further improve the convergence performance of the SAF-SNLMS, the variable step-size scheme is introduced. Simulation results demonstrate the SAF-SNLMS and its variable step-size variant obtain more robust performance when compared with the existing spline adaptive filter algorithms in impulsive noise.