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

Ying-Ren Chien (), Han-En Hsieh and Guobing Qian
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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

Mathematics, 2024, vol. 12, issue 9, 1-17

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.

Keywords: bias compensation; convex combination; impulse noise (IN); noisy inputs; process innovation; variable mixed-norm adaptive filtering algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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