Study of General Robust Subband Adaptive Filtering

Autor:	Yu, Yi, He, Hongsen, de Lamare, Rodrigo C., Chen, Badong
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 15 pages, 17 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.08856 Zobrazit plný text záznamu View this record from Arxiv