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A Scaled LMS Algorithm for Sparse System Identification with Impulsive Interference

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Abstract

A new LMS algorithm is proposed to improve the accuracy of the sparse system identification with impulse interference. The algorithm adopts a scaler to filter impulse interference, the scalar is also used for identifying sparse systems. The adaptive iteration can avoid significant changes since the abrupt impulsive interference in the channel estimation. Furthermore, the proposed method searches the sparse solution by considering the system sparsity and using an approximated \(\ell _0\) norm constraint. Because the proposed method combines sparse constraint and impulse interference, it gradually adjusts the step size according to the gradient. The effectiveness and superiority of the proposed algorithm are confirmed for sparse system identification with impulse interference in simulations.

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Data sharing is not applicable to this article as no datasets were generated. This article describes entirely theoretical research during the current study.

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Funding

This work was supported by the National Natural Science Foundation of China (Project No. 62171369,61701405).

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Fei-Yun Wu, Yan-Chong Song & Ru Peng

  2. Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an, Shaanxi, China

    Fei-Yun Wu, Yan-Chong Song & Ru Peng

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  1. Fei-Yun Wu
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  2. Yan-Chong Song
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  3. Ru Peng
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Correspondence to Fei-Yun Wu.

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Wu, FY., Song, YC. & Peng, R. A Scaled LMS Algorithm for Sparse System Identification with Impulsive Interference. Circuits Syst Signal Process 42, 4432–4441 (2023). https://doi.org/10.1007/s00034-023-02307-4

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  • DOI: https://doi.org/10.1007/s00034-023-02307-4

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