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Two Diffusion Proportionate Sign Subband Adaptive Filtering Algorithms

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Abstract

Recently, the diffusion sign subband adaptive filtering (DSSAF) algorithm has attracted great attention because of its robustness against the impulsive noise and its decorrelation power to the correlated input signals. However, the DSSAF converges slowly when the system to be identified is sparse. In order to solve the problem of slow convergence rate in the sparse system, this paper proposes a diffusion proportionate sign subband adaptive filtering (DPSSAF) algorithm by minimizing \(L_{1}\)-norm of the subband a posteriori error vector subject to weighted constraint for each node in the diffusion network. The proposed DPSSAF algorithm is robust against the impulsive noise in the sparse system. To further improve the performance of the DPSSAF in the very sparse system, an efficient version (EDPSSAF) based on \(L_{0}\)-norm is obtained, which can better measure the sparseness level of the unknown system with high sparsity. Simulation results are presented to illustrate the good performance of proposed algorithms.

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Acknowledgements

This work was partially supported by National Science Foundation of P. R. China (Grants: 61571374, 61271340, 61433011).

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Authors and Affiliations

  1. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Chengdu, China

    Long Shi & Haiquan Zhao

  2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

    Long Shi & Haiquan Zhao

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  1. Long Shi
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  2. Haiquan Zhao
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Correspondence to Haiquan Zhao.

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Shi, L., Zhao, H. Two Diffusion Proportionate Sign Subband Adaptive Filtering Algorithms. Circuits Syst Signal Process 36, 4242–4259 (2017). https://doi.org/10.1007/s00034-017-0494-9

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  • DOI: https://doi.org/10.1007/s00034-017-0494-9

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