Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6958214 | Signal Processing | 2016 | 6 Pages |
Abstract
The conventional normalized subband adaptive filter (NSAF) using a constant step-size generally faces an inherent trade-off between the steady-state misalignment and the convergence rate. We propose herein a variable step-size NSAF algorithm by minimizing the mean-square deviation (MSD) between the optimal weight vector and the weight vector estimate with the utilization of the shrinkage denoising technique. With the estimation error involved in the step-size adaptation for each subband individually, the proposed algorithm is capable of tracking non-stationary environments. Without the explicit whitening assumption of the input signal in each subband, the proposed algorithm exhibits low steady-state MSD even when the input signal of each subband is colored. Simulation results validate the low misalignment and good tracking ability of the proposed algorithm in system identification application.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Wei Xia, Lingfeng Zhu, JuLei Zhu, Jinfeng Hu, Huiyong Li,