Cited by 121Advanced Adaptive Filtering TechniquesBlind Source Separation TechniquesControl Systems and Identification

Adaptive multi-channel least mean square and Newton algorithms for blind channel identification

Huang, Yiteng Arden, Benesty, Jacob·2002·Signal Processing

Integrity signals

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Crossref · 10.1016/s0165-1684(02)00247-5

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18 of 18 references verified0 citation errors · 0 not checkable

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  • With the MCLMS algorithm, the channel estimate can converge in mean to the true impulse responses (up to a scale and common delay).

    “It was shown that with this MCLMS algorithm the channel estimate can converge in mean to the true impulse responses (up to a scale and common delay).”

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Authors
Huang, Y. A., & Benesty, J.
Affiliations
Alcatel-Lucent (France) (FR)
Corresponding author
Huang, Yiteng Arden
Venue
Signal Processing
Publisher
Elsevier BV
Published
2002-08
Volume
82
Issue
8
Pages
1127-1138
ISSN
0165-1684 (ISSN-L)
OpenAlex
W2140983050
Cited by
121
References
18
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 6 · 2018: 4 · 2017: 1 · 2016: 1 · 2015: 7 · 2014: 9 · 2013: 10 · 2012: 8
FWCI
6.59
Type
journal article
Language
en
Open access
closed
Locations
Signal Processing (publishedVersion)
Topics
  • Physical Sciences › Engineering › Computational Mechanics › Advanced Adaptive Filtering Techniques
  • Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords
blind system identification, blind channel estimation, LMS algorithm, Newton algorithm, FIR systems, channel impulse response estimation, mean convergence

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