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A statistical approach to subspace based blind identification

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2 Author(s)
Kristensson, M. ; Signal Processing Group, R. Inst. of Technol., Stockholm, Sweden ; Ottersten, B.

Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches

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Signal Processing, IEEE Transactions on  (Volume:46 ,  Issue: 6 )