A consideration on the blind estimation of single-input double-output system using orthogonal direct sum decomposition of received signal space
N. Tanabe, Takeshi Furukawa, Shigeo Tsujii · 2002
Subspace methods with second-order statistics based on principal component analysis (PCA) basically need to calculate the eigenvalues and the eigenvectors of the autocorrelation matrix of the received signal. However, the calculation of the eigenvalues and the eigenvectors of the matrix requires much computational complexity. We propose a new algorithm based on PCA without solving the eigenvalues and the eigenvectors of the matrix. Moreover, we perform the proposed method under the condition that noise-variance is known, but we confirm that the proposed method is effective to a certain degree when noise-variance is unknown. We show the effectiveness of the proposed method by numerical examples.