Study of General Robust Subband Adaptive Filtering

• URL: http://arxiv.org/abs/2208.08856v2
• Date: Fri, 19 Aug 2022 11:37:08 GMT
• Title: Study of General Robust Subband Adaptive Filtering
• Authors: Yi Yu, Hongsen He, Rodrigo C. de Lamare, Badong Chen
• Abstract summary: We propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise. By choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria, we can easily obtain different GR-SAF algorithms. 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.
• Score: 47.29178517675426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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