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

Shin, JaeWook; Park, Bum Yong; Lee, Won Il; Yoo, Jinwoo; Cho, Jaegeol

doi:10.1109/access.2022.3143136

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…which is known as the joint-optimization approach [15,17,24]. Both Equations ( 20) and ( 21) produced the same result, as follows:…”

Section: Proposed Nlms Algorithmmentioning

confidence: 88%

“…We considered that the unknown system vector w o (n) at index n had the following model [15][16][17]:…”

Section: Msd Analysis Of the Nlms Algorithm Using The Random Walk Modelmentioning

confidence: 99%

“…To obtain the MSD of the NLMS algorithm at index n, we considered the following three assumptions [17,[19][20][21][22][23]: Assumption 1. The signals v(n), u(n), q(n), and w(n − 1) are statistically independent.…”

Section: Msd Analysis Of the Nlms Algorithm Using The Random Walk Modelmentioning

confidence: 99%

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A Novel NLMS Algorithm for System Identification

et al. 2023

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In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes.

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“…which is known as the joint-optimization approach [15,17,24]. Both Equations ( 20) and ( 21) produced the same result, as follows:…”

Section: Proposed Nlms Algorithmmentioning

confidence: 88%

“…We considered that the unknown system vector w o (n) at index n had the following model [15][16][17]:…”

Section: Msd Analysis Of the Nlms Algorithm Using The Random Walk Modelmentioning

confidence: 99%

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A Novel NLMS Algorithm for System Identification

et al. 2023

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“…Adaptive filtering algorithms are attracting considerable interest in numerous signal processing fields [1], [2], [3], [4], [5]. And due to the simple structure and implementation, the least mean square (LMS) and normalized least mean square (NLMS) algorithms have already gained broad application [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Combined Regularization Factor for Affine Projection Algorithm Using Variable Mixing Factor

et al. 2022

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The affine projection algorithm with a fixed regularization parameter is subject to a compromise concerning the convergence speed and steady-state misalignment. To address this problem, we propose to employ a variable mixing factor to adaptively combine two different regularization factors in an attempt to put together the best properties of them. The selection of the mixing factor is derived by minimizing the energy of the noise-free a posteriori error, and for the sake of suppressing large fluctuations, a movingaverage method is designed for updating the mixing factor. Based on a random walk model, we also prove that the proposed mixing factor is as well available for the non-stationary system. The mathematical analysis including the stability performance, steady-state mean square error, and computational complexity are performed. In practice, we compare with the existing related algorithms in system identification and echo cancellation scenarios, the results illustrate that the proposed algorithm outperforms them with notable margins.

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A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

Lv²

2022

Journal of the Franklin Institute

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

Cited by 4 publications

References 32 publications

A Novel NLMS Algorithm for System Identification

A Novel NLMS Algorithm for System Identification

Combined Regularization Factor for Affine Projection Algorithm Using Variable Mixing Factor

A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

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