  This paper proposes a unified sparsity-aware robust normalized subband
adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems
under impulsive noise. The proposed SA-RNSAF algorithm generalizes different
algorithms by defining the robust criterion and sparsity-aware penalty.
Furthermore, by alternating optimization of the parameters (AOP) of the
algorithm, including the step-size and the sparsity penalty weight, we develop
the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also
obtains low steady-state misadjustment for sparse systems. Simulations in
various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm
outperforms existing techniques.
