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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.
Highlights
Adaptive filter algorithms have been used in a wide range of signal processing applications, such as acoustic echo cancellation, system identification, and channel equalization [1]–[5]
We propose an normalized subband adaptive filter (NSAF) algorithm that controls both the step size and regularization parameter owing to improvements in terms of tracking speed and misalignment
The proposed method is derived from a method similar to that of the adaptive regularization NSAF algorithm [26]; we propose a method that considers the step size and non-stationary systems to obtain both the optimal step size and regularization parameter at each iteration
Summary
Adaptive filter algorithms have been used in a wide range of signal processing applications, such as acoustic echo cancellation, system identification, and channel equalization [1]–[5]. The normalized least-mean-square (NLMS) algorithm is one of the most widely used adaptive filter algorithms owing to its low computational complexity and ease of implementation [6], [7] It exhibits substantial performance degradation in terms of the convergence rates for highly correlated input signals. To address this problem, affine projection (AP) algorithm and normalized subband adaptive filter (NSAF) algorithm were introduced [3], [8], [9]. We propose an NSAF algorithm that controls both the step size and regularization parameter owing to improvements in terms of tracking speed and misalignment.
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