A new subband adaptive filtering algorithm for sparse system identification with impulsive noise (Q2336651)

Summary: 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.