A New Transform-Domain Regularized Recursive Least M-Estimate Algorithm for a Robust Linear Estimation

Chan, S.C.; Zhang, Z G; Chu, Y. J.

doi:10.1109/tcsii.2011.2106314

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Cited by 19 publications

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“…effect of impulsive noise on the filter weights [102], [103], [104], [105]. Such algorithms are essentially hybrid techniques based on L 1 and L 2 -norms and several variants have been presented [106], [107], [108].…”

Section: Introductionmentioning

confidence: 99%

Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate

Lu¹,

Chen²,

Lamare³

et al. 2022

Preprint

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We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.

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