Interference-normalized least mean square algorithm

Jean Marc Valin*, Iain B. Collings

*Corresponding author for this work

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

An interference-normalized least mean square (INLMS) algorithm for robust adaptive filtering is proposed. The INLMS algorithm extends the gradient-adaptive learning rate approach to the case where the signals are nonstationary. In particular, we show that the INLMS algorithm can work even for highly nonstationary interference signals, where previous gradient-adaptive learning rate algorithms fail.

Original languageEnglish
Pages (from-to)988-991
Number of pages4
JournalIEEE Signal Processing Letters
Volume14
Issue number12
DOIs
Publication statusPublished - Dec 2007
Externally publishedYes

Keywords

  • Adaptive filtering
  • Adaptive filters
  • Gradient analysis
  • Gradient-adaptive learning rate
  • Learning systems
  • Least mean squares method
  • Normalized least mean square (NLMS) algorithm

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