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The design of optimal reduced-rank minimum variance beamformers based on the Krylov-subspace spanned by the covariance matrix of the array observations is addressed. We concentrate on finite sample-support situations that naturally appear in practice when the number of antennas and the sample-size are comparable in magnitude. The design of the coefficients of the resulting polynomial expansion is...
The design of optimal reduced-rank minimum variance beamformers based on the Krylov-subspace spanned by the covariance matrix of the array observations is addressed. We concentrate on finite sample-support situations that naturally appear in practice when the number of antennas and the sample-size are comparable in magnitude. The design of the coefficients of the resulting polynomial expansion is...
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