  This paper proposes a unified sparsity-aware robust recursive least-squares
RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive
noise. The proposed algorithm generalizes multiple algorithms only by replacing
the specified criterion of robustness and sparsity-aware penalty. Furthermore,
by jointly optimizing the forgetting factor and the sparsity penalty parameter,
we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only
exhibits low misadjustment but also can track well sudden changes of a sparse
system. Simulations in impulsive noise scenarios demonstrate that the proposed
S-RRLS and JO-S-RRLS algorithms outperform existing techniques.
