Steady-state mean-square performance analysis of the block-sparse maximum Versoria criterion

Ben Xue Su, Fei Yun Wu, Kun De Yang, Tian Tian, Yi Yang Ni

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The maximum Versoria criterion algorithm (MVC) exhibits lower steady-state misalignment and less complexity as compared to the maximum correntropy criterion (MCC) algorithm in the scenario of non-Gaussian impulsive noises. However, few scholars have discussed improving the MVC algorithm in sparse channels. This paper presents a block-sparse MVC (BS-MVC) algorithm by introducing the regularization norm, which has desirable performance in block-sparse channels and has excellent robustness to non-Gaussian conditions with impulsive noises. The steady-state excess mean-square error (EMSE) is discussed in Gaussian and non-Gaussian noise conditions. The effectiveness of BS-MVC and the theoretical expression is validated using multiple simulations. The BS-MVC achieves a lower steady-state mean-square deviation (MSD) and maintains a fast convergence rate compared with MCC and MVC algorithms.

Original languageEnglish
Article number109186
JournalSignal Processing
Volume213
DOIs
StatePublished - Dec 2023

Keywords

  • Maximum correntropy criterion (MCC)
  • Maximum Versoria criterion (MVC)
  • Steady-state excess mean-square error (EMSE)
  • ℓ-Norm constraint

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