Interference-normalized least mean square algorithm

Jean Marc Valin; Iain B. Collings

doi:10.1109/LSP.2007.908017

Interference-normalized least mean square algorithm

Jean Marc Valin^*, Iain B. Collings

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

18 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 language	English
Pages (from-to)	988-991
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	14
Issue number	12
DOIs	https://doi.org/10.1109/LSP.2007.908017
Publication status	Published - Dec 2007
Externally published	Yes

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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10.1109/LSP.2007.908017

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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.",

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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year = "2007",

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AB - 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.

KW - Adaptive filtering

KW - Adaptive filters

KW - Gradient analysis

KW - Gradient-adaptive learning rate

KW - Learning systems

KW - Least mean squares method

KW - Normalized least mean square (NLMS) algorithm

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JF - IEEE Signal Processing Letters

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Interference-normalized least mean square algorithm

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Keywords

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