  In this paper, we propose a general robust subband adaptive filtering
(GR-SAF) scheme against impulsive noise by minimizing the mean square deviation
under the random-walk model with individual weight uncertainty. Specifically,
by choosing different scaling factors such as from the M-estimate and maximum
correntropy robust criteria in the GR-SAF scheme, we can easily obtain
different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be
reduced to a variable regularization robust normalized SAF algorithm, thus
having fast convergence rate and low steady-state error. Simulations in the
contexts of system identification with impulsive noise and echo cancellation
with double-talk have verified that the proposed GR-SAF algorithms outperforms
its counterparts.
