Abstract:

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.