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Unsupervised robust recursive least-squares algorithm for impulsive noise filtering

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Abstract

A robust recursive least-squares (RLS) adaptive filter against impulsive noise is proposed for the situation of an unknown desired signal. By minimizing a saturable nonlinear constrained unsupervised cost function instead of the conventional least-squares function, a possible impulse-corrupted signal is prevented from entering the filter’s weight updating scheme. Moreover, a multi-step adaptive filter is devised to reconstruct the observed “impulse-free” noisy sequence, and whenever impulsive noise is detected, the impulse contaminated samples are replaced by predictive values. Based on simulation and experimental results, the proposed unsupervised robust recursive least-square adaptive filter performs as well as conventional RLS filters in “impulse-free” circumstances, and is effective in restricting large disturbances such as impulsive noise when the RLS and the more recent unsupervised adaptive filter fails.

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Jie Chen, Tao Ma, WenJie Chen & ZhiHong Peng

  2. Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology), Ministry of Education, Beijing, 100081, China

    Jie Chen, Tao Ma, WenJie Chen & ZhiHong Peng

Authors
  1. Jie Chen
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  2. Tao Ma
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  3. WenJie Chen
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  4. ZhiHong Peng
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Correspondence to Tao Ma.

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Chen, J., Ma, T., Chen, W. et al. Unsupervised robust recursive least-squares algorithm for impulsive noise filtering. Sci. China Inf. Sci. 56, 1–10 (2013). https://doi.org/10.1007/s11432-011-4458-6

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  • DOI: https://doi.org/10.1007/s11432-011-4458-6

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