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Sparsity aware normalized least mean p-power algorithms with correntropy induced metric penalty

  • Xi'an Jiaotong University
  • Akita Prefectural University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

For identifying the non-Gaussian impulsive noise systems, normalized least mean p-power (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is developed without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise synchronously, this paper proposes two effective NLMP-type algorithms. The first one is correntropy induced metric (CIM) constraint NLMP (CIMNLMP) algorithm. The second one is an improved CIM constraint variable regularized NLMP (CIMVRNLMP) algorithm, in which variable regularized parameter (VRP) is selected to adjust convergence speed and steady-state error. Numerical simulations are given to confirm the two proposed algorithms.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Digital Signal Processing, DSP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages638-642
Number of pages5
ISBN (Electronic)9781479980581, 9781479980581
DOIs
StatePublished - 9 Sep 2015
EventIEEE International Conference on Digital Signal Processing, DSP 2015 - Singapore, Singapore
Duration: 21 Jul 201524 Jul 2015

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2015-September

Conference

ConferenceIEEE International Conference on Digital Signal Processing, DSP 2015
Country/TerritorySingapore
CitySingapore
Period21/07/1524/07/15

Keywords

  • correntropy induced metric (CIM)
  • non-Gaussian impulsive noise
  • normalized least mean p-power (NLMP)
  • sparse parameter estimation
  • variable regularized parameter (VRP)

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