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Variable tap-length adaptive algorithm which exploits both second and fourth order statistics

A new variable tap-length adaptive algorithm which exploits both second and fourth order statistics is proposed in this paper. In this algorithm, the tap-length of the adaptive filter is varying rather than fixed, and controlled by fourth order statistics, whereas the coefficient update retains a conventional least mean square (LMS) form. As will be seen in the simulation results, the proposed algorithm has a faster convergence rate as compared with an existing variable tap-length LMS algorithm which is based only on second order statistics in sub-Gaussian noise environments.