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Robust Bias Compensation Method for Sparse Normalized Quasi-Newton Least-Mean with Variable Mixing-Norm Adaptive Filtering

Author

Listed:
  • Ying-Ren Chien

    (Department of Electrical Engineering, National Ilan University, Yilan 260007, Taiwan)

  • Han-En Hsieh

    (Department of Electrical Engineering, National Ilan University, Yilan 260007, Taiwan)

  • Guobing Qian

    (College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

Abstract

Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address these issues. We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combination of adaptive filters’ step sizes, we propose a novel variable mixing-norm method, BC-SNQNLM-VMN, to accelerate the convergence of our BC-SNQNLM algorithm. Simulation results confirm that the proposed method significantly outperforms other comparative works regarding normalized mean-squared deviation (NMSD) in the steady state.

Suggested Citation

  • Ying-Ren Chien & Han-En Hsieh & Guobing Qian, 2024. "Robust Bias Compensation Method for Sparse Normalized Quasi-Newton Least-Mean with Variable Mixing-Norm Adaptive Filtering," Mathematics, MDPI, vol. 12(9), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1310-:d:1382966
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