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Circuits, Systems, and Signal Processing

, Volume 38, Issue 4, pp 1736–1750 | Cite as

Diffusion Sparse Sign Algorithm with Variable Step-Size

  • Feng Chen
  • Xiaoxia Liu
  • Shukai DuanEmail author
  • Lidan Wang
  • Jiagui Wu
Article
  • 82 Downloads

Abstract

In this paper, we propose the diffusion sparse sign algorithm with variable step-size for distributed estimation in sparse and impulsive interference environments. Firstly, we address the problem of in-network distributed estimation for sparse vectors under the impulsive noise environment. In order to exploit the sparsity of the vector of interest, we incorporate the sparse norms (\({l_1}\)-norm and \(RW{l_1}\)-norm) into the cost function of the standard diffusion sign algorithm, which accelerates the convergence speed of zero or near-zero components. In addition, we propose the adaptive variable step-size to further improve the convergence rate of the proposed algorithm. The variable step-size is derived by the correlation entropy, which contains a modified Gaussian kernel function and is robust to impulsive noise. In this paper, every node combines its correlation entropy function with the information of its neighborhood to drive the variable step-size at each iteration. Simulation results show that the proposed algorithm outperforms the standard diffusion SA in the sparse and impulsive system and the convergence rate of the proposed algorithm is faster than constant step-size algorithms.

Keywords

Diffusion Sparsity Variable step-size Impulsive noise Sign algorithm 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Feng Chen
    • 1
    • 2
    • 3
    • 4
  • Xiaoxia Liu
    • 1
    • 3
  • Shukai Duan
    • 1
    Email author
  • Lidan Wang
    • 1
  • Jiagui Wu
    • 1
    • 3
  1. 1.College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.School of Mathematics and StatisticsSouthwest UniversityChongqingChina
  3. 3.Key Laboratory of Nonlinear Circuits and Intelligent Information ProcessingChongqingChina
  4. 4.Chongqing Collaborative Innovation Center for Brain ScienceChongqingChina

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