Optimal design of NLMS algorithm with a variable scaler against impulsive interference

Wu, Fei-Yun; Song, Yaru

doi:10.1007/s11760-023-02487-1

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Song et al [27] developed a fast threshold orthogonal matching pursuit (OMP) based on a self-learning dictionary for propeller signal reconstruction, demonstrating the efficiency of their approach. Wu and Song [28] designed an optimal normalized least mean squares (NLMS) algorithm with a variable scaler to counteract impulsive interference, improving the robustness of the algorithm. Song et al [29] proposed a neighborhoodbased multiple orthogonal least squares method for sparse signal recovery, which enhances the recovery accuracy in sparse systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive block sparse system identification in an impulsive interference environment

WU,

Song

2024

Preprint

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To improve the accuracy of sparse system identification with impulsive measurement interference, a scaled least mean square (LMS) algorithm with block sparsity constraint is proposed. The proposed method adopts a scalar for scaling and filtering the impulsive interference. The proposed scalar avoids drastic changes in channel estimation due to the wrong information from the abrupt impulsive interference. On the other hand, by exploiting the sparsity of the system, the proposed block sparsity constraint searches sparse solution by using an approximated block $\ell_0$ norm constraint. Additionally, the step-size of the proposed method is gradually adjusted based on the gradient factors combining block sparse constraint and impulsive interference. Simulations, including sparse system identification and tracking, are conducted to confirm the effectiveness of the proposed algorithm for sparse system identification with impulsive interference. Moreover, this paper includes a comparison and analysis of underwater acoustic channel estimation results, demonstrating the superiority and effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%