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Robust competitive diffusion LMS algorithm

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Abstract

The main drawback of the diffusion least mean square algorithm (DLMS) is its performance will degrade when the impulsive noise (IN) occurs in the system. To improve the performance, a robust competitive DLMS (RCDLMS) algorithm based on two types of effective IN detection method is proposed. Because both methods estimate noise variance to determine whether impulse noise occurs or not. The first method is using the shrinkage denoising method to obtain the variance of noise, and the second method is by estimating cross-correlation of the input and error signals to acquire the variance of noise. Finally, simulation results demonstrate that the proposed algorithm has a good tracking capability and robust performance against impulsive noise.

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Authors and Affiliations

  1. Sichuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China

    Pengwei Wen, Jiashu Zhang & Sheng Zhang

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  1. Pengwei Wen
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  2. Jiashu Zhang
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  3. Sheng Zhang
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Correspondence to Pengwei Wen.

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This work was partially supported by National Science foundation of P. R. China (Grants: 61671392, 61801401), in part by the Fundamental Research Funds for the Central Universities under Grant 2682018CX22 and in part by the Doctoral Innovation Fund Program of Southwest Jiaotong University.

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Wen, P., Zhang, J. & Zhang, S. Robust competitive diffusion LMS algorithm. SIViP 14, 343–349 (2020). https://doi.org/10.1007/s11760-019-01556-8

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  • DOI: https://doi.org/10.1007/s11760-019-01556-8

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