System and method for blind identification of multichannel...

Pulse or digital communications – Receivers – Interference or noise reduction

Reexamination Certificate

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C375S260000, C375S316000, C375S324000, C375S349000

Reexamination Certificate

active

08045661

ABSTRACT:
A system for, and method of, blind identification of multichannel finite impulse response filters. In one embodiment, the system includes: (1) a decomposition generator configured to construct, from noisy symbols containing data, a block-Hankel structured matrix and a decomposition thereof, (2) an iterative matrix re-estimator coupled to the decomposition generator and configured iteratively to re-estimate singular vectors corresponding to a scalar that satisfies the block-Hankel matrix until a sufficient convergence occurs, (3) a structured low-rank estimate matrix generator coupled to the iterative matrix re-estimator and configured to generate a block-Hankel structured low-rank estimate matrix related to the block-Hankel structured matrix and (4) an STLS channel estimator coupled to the structured low-rank estimate matrix generator and configured to obtain STLS channel estimates from the block-Hankel structured low-rank estimate matrix.

REFERENCES:
patent: 6990061 (2006-01-01), Deneire et al.
patent: 7383178 (2008-06-01), Visser et al.
Lang Tong, et al., “A New Approach to Blind Identification and Equalization of Multipath Channels” IEEE, pp. 856-860, 1991.
Yingbo Hua, “Fast Maximum Likelihood for Blind Identification of Multiple FIR Channels” IEEE Transactions on Signal Processing, vol. 44, No. 3, pp. 661-672, Mar. 1996.
Guanghan Xu, et al., “A Least-Squares Approach to Blind Channel Identification” IEEE Transactions on Signal Processing, vol. 43, No. 12, pp. 2982-2993, Dec. 1995.
Jacob H. Gunther, et al., “A Structured Least-Squares Approach to Blind Channel Identification and Equalization” Proc. 37th Asilomar Conf. Signals, Syst. Comp., pp. 45-49, 2003.
Theagenis J. Abatzoglou, et al., “The Constrained Total Least Squares Technique and Its Applications to Harmonic Superresolution” IEEE Transactions on Signal Processing, vol. 39, No. 5, pp. 1070-1087, May 1991.
Bart De Moor, “Total Least Squares for Affinely Structured Matrices and the Noisy Realization Problem” IEEE Transactions on Signal Processing, vol. 42, No. 11, pp. 3104-3113, Nov. 1994.

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