Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization
This is an incremental improvement for signal processing applications dealing with sparse systems in noisy environments.
The paper tackles the problem of identifying sparse systems under impulsive noise by proposing a unified sparsity-aware robust normalized subband adaptive filtering algorithm, which through alternating optimization achieves faster convergence and lower steady-state misadjustment than existing techniques.
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.