A least-squares approach to blind channel identification
Abstract
Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented.
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The two channel impulse responses h0 and h1 can be uniquely determined (up to a scale and a common delay) from the covariance relation if the polynomials formed from h0 and h1 are coprime and the autocorrelation matrix of the source signal is of full rank.
“It has been shown that the two channel impulse responses (i.e., h 0 and h 1) can be uniquely determined (up to a scale and a common delay) from (32) if the following two conditions hold [56-58]:”
From Time Delay Estimation in Room Acoustic Environments: An Overview (2006) · cites this paper as [56]
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Time Delay Estimation in Room Acoustic Environments: An Overview
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- Authors
- Xu, G., Liu, H., Tong, L., & Kailath, T.
- Affiliations
- The University of Texas at Austin (US)
- University of Connecticut (US)
- Stanford University (US)
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1995
- Volume
- 43
- Issue
- 12
- Pages
- 2982-2993
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2046791564
- Cited by
- 777
- References
- 29
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 5 · 2023: 7 · 2022: 12 · 2021: 20 · 2020: 15 · 2019: 21 · 2018: 26 · 2017: 19 · 2016: 13 · 2015: 20 · 2014: 17 · 2013: 19 · 2012: 32
- FWCI
- 44.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Computational Mechanics › Advanced Adaptive Filtering Techniques
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Keywords
- blind channel estimation, least squares identification, identifiability conditions
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