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An improved proportionate least mean p-power algorithm for adaptive filtering

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Abstract

The least mean p-power error criterion has been successfully used in adaptive filtering due to its strong robustness against large outliers. In this paper, we develop a new adaptive filtering algorithm, named the proportionate least mean p-power (PLMP) algorithm, which uses the mean p-power error as the adaptation cost function. Compared with the standard proportionate normalized least mean square algorithm, the PLMP can achieve much better performance in terms of the mean square deviation, especially in the presence of impulsive non-Gaussian noises. The mean and mean square convergence of the proposed algorithm are analyzed, and some related theoretical results are also obtained. Simulation results are presented to verify the effectiveness of our proposed algorithm.

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Acknowledgements

This work was supported by 973 Program (No. 2015CB351703) and National Natural Science Foundation of China (Nos. 61372152, 61271210).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

    Xie Zhang, Siyuan Peng & Yuli Fu

  2. School of Automation, Guangdong University of Technology, Guangzhou, China

    Zongze Wu & Yajing Zhou

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  1. Xie Zhang
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  2. Siyuan Peng
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  3. Zongze Wu
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  5. Yuli Fu
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Correspondence to Zongze Wu.

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Zhang, X., Peng, S., Wu, Z. et al. An improved proportionate least mean p-power algorithm for adaptive filtering. SIViP 12, 59–66 (2018). https://doi.org/10.1007/s11760-017-1130-7

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  • DOI: https://doi.org/10.1007/s11760-017-1130-7

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