A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise

Abstract

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 $l_1$-norm optimization and $l_0$-norm penalty of the weight vector in the cost function, the proposed $l_0$-norm sign SAF ($l_0$-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 $l_0$-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.