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A Family of Robust Diffusion Adaptive Filtering Algorithms Based on the Tanh Framework

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Abstract

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the “Tanh cost function framework.” By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms.

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All data generated or analyzed during this study are included in this article. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant No.: 62267007), Natural Science Foundation of Gansu Province (Grant No.: 23JRRA692).

Funding

National Natural Science Foundation of China (62267007), Yuanlian Huo, Natural Science Foundation of Gansu Province (23JRRA692), Yuanlian Huo.

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Author notes

  1. Tianci Xu, Yurong Xu, Yongfeng Qi and Ruibo Ding have contributed equally to this work.

Authors and Affiliations

  1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730000, China

    Yuanlian Huo, Tianci Xu & Ruibo Ding

  2. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730000, China

    Yongfeng Qi & Yurong Xu

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  1. Yuanlian Huo
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  2. Tianci Xu
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  3. Yongfeng Qi
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Correspondence to Tianci Xu.

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Huo, Y., Xu, T., Qi, Y. et al. A Family of Robust Diffusion Adaptive Filtering Algorithms Based on the Tanh Framework. Circuits Syst Signal Process 43, 1938–1956 (2024). https://doi.org/10.1007/s00034-023-02552-7

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