A new subband adaptive filtering algorithm for sparse system identification with impulsive noise

This paper presents a novel subband adaptive filter (saf) for system identification where an impulse response is sparse and disturbed with an impulsive noise. benefiting from the uses of l1-norm optimization and l0-norm penalty of the weight vector in the cost function,the proposed l0-norm sign saf (l0-ssaf) achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. simulation results in the system identification scenario confirm that the proposed l0-norm ssaf is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise. © 2014 young-seok choi.