Data block adaptive filtering algorithms for α-stable random processes

Abstract

The least mean p-norm (LMP) algorithm is an effective algorithm for processing the signal of @a-stable distribution. This paper proposes data block adaptive filtering algorithms for the @a-stable random processes based on the fractional lower order statistics (FLOS). The data block algorithms change the direction of coefficient increment vector by introducing a matrix which includes the information of more past input signal vectors than which are used in the LMP algorithm during the iteration process, taking full advantage of the past values of the gradient vector during the adaptation. Simulations studies indicate that the proposed algorithms increase convergence rate in non-Gaussian stable distribution noise environments compared to the existing algorithms based on FLOS summarized in this paper.