A Novel Normalized Subband Adaptive Filter Algorithm Based on the Joint-Optimization Schemeopen access
- Authors
- Shin, Jaewook; Park, Bum Yong; Lee, Won Il; Yoo, Jinwoo; Cho, Jaegeol
- Issue Date
- Jan-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Signal processing algorithms; Adaptive filters; Covariance matrices; System identification; Convergence; Licenses; Indexes; Adaptive filter; normalized subband adaptive filter; variable step size; variable regularization parameter; mean-square deviation
- Citation
- IEEE ACCESS, v.10, pp 9868 - 9876
- Pages
- 9
- Journal Title
- IEEE ACCESS
- Volume
- 10
- Start Page
- 9868
- End Page
- 9876
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21392
- DOI
- 10.1109/ACCESS.2022.3143136
- ISSN
- 2169-3536
- Abstract
- Herein, we propose a normalized subband adaptive filter (NSAF) algorithm that adjusts both the step size and regularization parameter. Based on the random-walk model, the proposed algorithm is derived by minimizing the mean-square deviation of the NSAF at each iteration to calculate the optimal parameters. We also propose a method for estimating the uncertainty in an unknown system. Consequently, the proposed algorithm improves performance in terms of tracking speed and misalignment. Simulation results show that the proposed NSAF outperforms existing algorithms in system identification scenarios.
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