A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method.

• To overcome the trade-off between fast convergence and low steady-state misalignment under the fixed step-size, we devised a family of variable step-size strategy S-IWF-SSAF algorithms based on the transient model of algorithms by minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. • In order to further enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. And to guarantee the convergence, the stable step-size range in the mean and mean square sense is concluded. In addition, the computational requirements are exhibited as well. • Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to state-of-the-art algorithms within various system inputs under impulsive interference environments. Recently, the sparsity-aware sign subband adaptive filter algorithm with individual-weighting-factors (S-IWF-SSAF) was devised. To accomplish performance enhancement, the variable parameter S-IWF-SSAF (VP-S-IWF-SSAF) algorithm was developed through optimizing the step-size and penalty factor, respectively. Different from the optimization scheme, we devise a family of variable step-size strategy S-IWF-SSAF (VSS-S-IWF-SSAF) algorithms based on the transient model of algorithms via minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. And in order to enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. It is worth mentioning that the presented algorithms could acquire lower computational requirements and exhibit higher steady-state estimation accuracy obviously and acceptable tracking characteristic in comparison to the VP-S-IWF-SSAF algorithm. In addition, the stable step-size range in the mean and mean square sense and steady-state performance are concluded. And the computational requirements are exhibited as well. Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to other related algorithms within various system inputs under impulsive interference environments. [ABSTRACT FROM AUTHOR]