Singular Value Decomposition of Covariance Matrix for Arbitrary Array Output Data
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Resource Overview
Perform singular value decomposition on the covariance matrix of arbitrary array output data to obtain eigenvalues and eigenvectors, then construct a spatial spectrum function utilizing the orthogonality between signal subspace and noise subspace with MATLAB implementation
Detailed Documentation
In this document, we perform singular value decomposition (SVD) on the covariance matrix of output data from arbitrary arrays to calculate eigenvalues and eigenvectors. By leveraging the orthogonality between signal subspace and noise subspace, we construct a spatial spectrum function. The implementation typically involves computing the covariance matrix using MATLAB's cov() function, applying svd() for decomposition, and then using eig() to extract eigenvalues/vectors for subspace separation. These steps enable enhanced analysis and understanding of data characteristics and spatial structure through spectral estimation techniques.
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