In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts.
@article{arxiv.2208.08856,
title = {Study of General Robust Subband Adaptive Filtering},
author = {Yi Yu and Hongsen He and Rodrigo C. de Lamare and Badong Chen},
journal= {arXiv preprint arXiv:2208.08856},
year = {2022}
}