access icon free Set-membership normalised least M-estimate spline adaptive filtering algorithm in impulsive noise

This letter proposes a set-membership normalised least M-estimate algorithm based on Wiener spline adaptive filter (SAF). The proposed algorithm combines the set-membership framework and least-M estimate scheme, thus achieving faster convergence rate and effective suppression of impulsive noise on the filter weight and control point adaptation. Simulation results demonstrate that the proposed one exhibits more robust performance compared to the conventional SAF algorithms in an impulsive noise environment.

Inspec keywords: Wiener filters; splines (mathematics); adaptive filters; impulse noise

Other keywords: control point adaptation; filter weight; set-membership normalised least M-estimate spline adaptive filtering algorithm; Wiener spline adaptive filter; impulsive noise environment; SAF algorithm

Subjects: Interpolation and function approximation (numerical analysis); Filtering methods in signal processing; Interpolation and function approximation (numerical analysis); Signal processing theory

References

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      • 9. Guan, S., Li, Z.: ‘Normalised spline adaptive filtering algorithm for nonlinear system identification’, Neural Process. Lett., 2017, 5, pp. 113.
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      • 4. Zou, Y., Chan, S.C., Ng, T.S.: ‘Least mean M-estimate algorithms for robust adaptive filtering in impulse noise’, Signal Process. Lett., 2000, 47, (12), pp. 15641569.
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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.4434
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