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A Novel Normalized Subband Adaptive Filter Algorithm Based on the Joint-Optimization Scheme
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DOI：10.1109/ACCESS.2022.3143136.png)
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摘要
En 中文
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.
Keyword:
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
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Variable step-size sign subband adaptive filter with subband filter selection.png)
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