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Unsupervised robust recursive least-squares algorithm for impulsive noise filtering
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DOI：10.1007/s11432-011-4458-6.png)
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Abstract
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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.
Keywords:
unsupervised adaptive filtering
impulsive noise suppression
recursive least-squares algorithm
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