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Nonlinear Spline Adaptive Filtering Against Non-Gaussian Noise

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Abstract

In this paper, a generalized maximum Versoria criterion algorithm (GMVC) based on wiener spline adaptive filter, called SAF–GMVC, is proposed. The proposed algorithm is used for nonlinear system identification under non-Gaussian environment. To improve the convergence performance of the SAF–GMVC, the momentum stochastic gradient descent (MSGD) is introduced. In order to further reduce the steady-state error, the variable step-size algorithm is introduced, called as SAF–GMVC–VMSGD. Simulation results demonstrate that SAF–GMVC–VMSGD achieves better filtering effective against non-Gaussian noise.

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Data Availability Statement

The data sets generated during and analyzed during the current study are available from the author on reasonable request.

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Acknowledgements

This work is funded by the Science, Technology and Innovation Commission of Shenzhen Municipality (Grant Nos. JCYJ20170815161351983), the National Natural Science Foundation of China (Grant Nos. U20B2040 and 61671379).

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  1. Research & Development Institute in Shenzhen, Northwestern Polytechnical University, Shenzhen, China

    Wenyan Guo & Yongfeng Zhi

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  1. Wenyan Guo
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  2. Yongfeng Zhi
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Correspondence to Yongfeng Zhi.

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Guo, W., Zhi, Y. Nonlinear Spline Adaptive Filtering Against Non-Gaussian Noise. Circuits Syst Signal Process 41, 579–596 (2022). https://doi.org/10.1007/s00034-021-01798-3

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