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Adaptive multi-channel least mean square and Newton algorithms for blind channel identification
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“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).”
From Time Delay Estimation in Room Acoustic Environments: An Overview (2006) · cites this paper as [60]
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Metadata
- Authors
- Huang, Y. A., & Benesty, J.
- Affiliations
- Alcatel-Lucent (France) (FR)
- Corresponding author
- Huang, Yiteng Arden
- ORCID
- Benesty, Jacob
- 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 › Computer Science › Signal Processing › 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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