Covariance structures for multidimensional data

A notation for describing covariance structures for a wide class of signal processing problems is established. The goal is the development of an organized notation and a general framework for the discussion of problems that involve multidimensional data and structured covariances under a complex Gaussian model. These structures are used as constraint sets for covariance matrices of naturally multidimensional data organized into column-vector form for signal processing applications. The structures involve a hierarchy of subblocks within the matrix and include block-circulant and block-Toeplitz matrices and their respective generalizations.< >