Abstract
The classical sign algorithm (SA) has attracted much attention in many applications because of its low computational complexity and robustness against impulsive noise. However, its steady-state mean-square derivation (MSD) is large when a large step-size is used to guarantee a relatively fast convergence rate. To address this problem, the dual sign algorithm (DSA) was developed by using a piecewise cost function in the literature. In this paper a family of normalized DSAs (NDSAs) is proposed to further improve the performance of the DSA in terms of MSD. Specifically, two sparse NDSAs are firstly developed, by using the ℓ1-norm and ℓ0-norm constraints, respectively; on this basis, some variable step-size algorithms are then proposed based on mean-square a posteriori error minimization. Finally, simulation results are provided to show the superior performance of our proposed algorithms.
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