Study of General Robust Subband Adaptive Filtering
This work addresses adaptive filtering robustness for signal processing applications, but it appears incremental as it builds on existing robust criteria.
The paper tackles the problem of adaptive filtering in the presence of impulsive noise by proposing a general robust subband adaptive filtering (GR-SAF) scheme, which achieves fast convergence and low steady-state error, as verified in simulations for system identification and echo cancellation.
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.