New RLS-like adaptive algorithm under alpha-stable noise conditions

A RLS-Like adaptive algorithm was developed to filter alpha-stable noises. Unlike previously introduced stochastic gradient-type algorithms, the method minimized the Lp norm of the error at the output of the adaptive filter instead of the usual Euclidean norm as the alpha-stable distribution without the second and higher order moment. The method concluded by way of the matrix inversion lemma can avoid the IRLS iteration calculation of other RLS-Like algorithms and greatly decrease computational complexity. The simulation results show that the convergence speed of the proposed algorithm is much faster than that of the LMP and NLMP algorithms.