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A filter proportionate LMS algorithm based on the arctangent framework for sparse system identification

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Abstract

For a physical sparse system identification issue, this brief proposes a filter proportionate arctangent framework-based least mean square (FP-ALMS) algorithm. The ALMS algorithm has significant robustness against impulsive noise, whereas the filter proportionate concept when utilized in combination with the ALMS takes advantage of the sparse feature to accelerate convergence time. As a result, it turns out that the FP-ALMS algorithm has greater robustness and convergence speed in an impulsive environment. Finally, simulation outcomes demonstrate that the novel FP-ALMS algorithm outperforms other existing algorithms in terms of robustness in an impulsive environment, convergence rate, and steady-state error for sparse system identification.

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Authors and Affiliations

  1. School of Electronics Engineering, KIIT Deemed to Be University, Bhubaneswar, India

    Rosalin & Ansuman Patnaik

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  1. Rosalin
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  2. Ansuman Patnaik
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The first author proposed the idea and wrote the manuscript text. The second author simulated the results.

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Correspondence to Ansuman Patnaik.

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Rosalin, Patnaik, A. A filter proportionate LMS algorithm based on the arctangent framework for sparse system identification. SIViP 18, 335–342 (2024). https://doi.org/10.1007/s11760-023-02729-2

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  • DOI: https://doi.org/10.1007/s11760-023-02729-2

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