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Sign-Normalized IIR Spline Adaptive Filtering Algorithms for Impulsive Noise Environments

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Abstract

In this paper, a new sign-normalized least-mean-square adaptive filtering algorithm based on IIR spline adaptive filter (IIR-SAF-SNLMS) is proposed. By using the absolute value of the a posteriori error as the cost function and solving the optimization problem, the proposed algorithm achieves robustness against impulsive noise. Furthermore, to further improve the performance of the IIR-SAF-SNLMS, its variable step-size variant is proposed. Simulation results in the identification of the IIR-SAF nonlinear model show that the proposed algorithms provide better tracking and steady-state performance as compared to the existing spline algorithms.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grant 61501119.

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Authors and Affiliations

  1. School of Electronic Engineering, Dongguan University of Techonology, No.1 DaXue Road, Dong’guan, 523808, Guangdong, China

    Chang Liu, Zhi Zhang & Xiao Tang

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  1. Chang Liu
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  2. Zhi Zhang
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  3. Xiao Tang
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Correspondence to Chang Liu.

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Liu, C., Zhang, Z. & Tang, X. Sign-Normalized IIR Spline Adaptive Filtering Algorithms for Impulsive Noise Environments. Circuits Syst Signal Process 38, 891–903 (2019). https://doi.org/10.1007/s00034-018-0874-9

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  • DOI: https://doi.org/10.1007/s00034-018-0874-9

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