Techniques of Eigenvalues Estimation and Association

Techniques of eigenvalues estimation and association (TEEA) are an essential part of a multidimensional spectral and array processing concept known as subspace rotation invariance (also known as ESPRIT and Matrix Pencils). Subspace rotation invariance is especially useful for designing flexible arrays for estimation of multidimensional source angles and polarizations. Other applications include synthetic aperture radar imaging, nuclear magnetic resonance imaging and wireless communications. All applications of subspace rotation invariance require TEEA to extract the desired information from data. In this paper, we review, refine, and extend a variety of TEEA available in the literature.