Subspace algorithms is minimized by the corresponding eigenvalues ​ ​ of all the noise variance-covariance matrix of a matrix decomposition method based on the feature space , subspace signal is received by the array data covariance matrix composed of the noise subspace and signal corresponding eigenvectors feature vector components. Subspace algorithm is the use of orthogonal properties between these two complementary space to estimate the orientation of the space signal

A_subspace_approach_to_blind_space-time_signal_processing_for_wireless_communication_system.pdf

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