The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Multichannel blind identification: from subspace to maximum likelihood methods
Abstract
A review of blind channel estimation algorithms is presented. From the (second-order) moment-based methods to the maximum likelihood approaches, under both statistical and deterministic signal models. We outline basic ideas behind several new developments, the assumptions and identifiability conditions required by these approaches, and the algorithm characteristics and their performance. This review serves as an introductory reference for this currently active research area.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
Garreau, G. A., & Rudin, W. (1987) · The Statistician · doi:10.2307/2348852 · Cited by 10,622
Kailath, T. (1980) · Cited by 6,215
Modeling by shortest data description
Rissanen, J. (1978) · Automatica · doi:10.1016/0005-1098(78)90005-5 · Cited by 6,011
Forney, G. D. (1973) · Proceedings of the IEEE · doi:10.1109/proc.1973.9030 · Cited by 5,647
Maximum Likelihood from Incomplete Data via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976) · Cited by 4,517
Adaptive filtering, prediction and control
Ioannou, P. (1985) · Automatica · doi:10.1016/0005-1098(85)90014-7 · Cited by 4,480
Cited by
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
Bajwa, W. U., Haupt, J., Sayeed, A. M., & Nowak, R. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2042415 · Cited by 1,152
Time Delay Estimation in Room Acoustic Environments: An Overview
Chen, J., Benesty, J., & Huang, Y. (2006) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/asp/2006/26503 · Cited by 370
A class of frequency-domain adaptive approaches to blind multichannel identification
Huang, Y., & Benesty, J. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.806559 · Cited by 199
Adaptive multi-channel least mean square and Newton algorithms for blind channel identification
Huang, Y. A., & Benesty, J. (2002) · Signal Processing · doi:10.1016/s0165-1684(02)00247-5 · Cited by 121
Convergence of stochastic-approximation-based algorithms for blind channel identification
Chen, H.-F., Cao, X.-R., & Zhu, J. (2002) · IEEE Transactions on Information Theory · doi:10.1109/18.995653 · Cited by 19
Metadata
- Authors
- Tong, L., & Perreau, S.
- Venue
- Proceedings of the IEEE
- Published
- 1998
- Volume
- 86
- Issue
- 10
- Pages
- 1951-1968
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2107757493
- Cited by
- 572
- References
- 57
- FWCI
- 26.14
- Type
- journal article
- Language
- en
- Open access
- closed
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