Gradient-norm based VSS-NLMP algorithm in α-stable environments

According to the problem of adaptive filtering in α stable environments, a gradient-norm based variable step-size normalized least mean p-norm (VSS-NLMP) algorithm is proposed. The squared norm of the smoothed gradient vector, which can track the variation of the mean square deviation at iteration, is used to update the step-size parameter in the algorithm. The weighted average of the gradient vector reduces the noise effectively and results in a more stable and less noisy adaptation of the step-size parameter. The update and convergence of the proposed algorithm are formulated. The simulation results indicate that the proposed algorithm has a better performance compared with the existing VSS-NLMP algorithms.