A Novel Normalized Subband Adaptive Filter Algorithm Based on the Joint-Optimization Scheme

IEEE Access

卷:10

Jinwoo Yoo¹  Won Il Lee²  Jaewook Shin²  Bum Yong Park²  Jaegeol Cho³ 

[1] Department of Automobile and IT Convergence, Kookmin University, Seoul, Republic of Korea;

[2] Department of Electronic Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, Republic of Korea;

[3] Department of Medical and Mechatronics Engineering, Soonchunhyang University, Asan, Chungnam, Republic of Korea;

关键词: Adaptive filter;    normalized subband adaptive filter;    variable step size;    variable regularization parameter;    mean-square deviation;   

DOI  :  10.1109/ACCESS.2022.3143136

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown