Eigenvalues of the sample covariance matrix for a towed array

It is well known that observations of the spatial sample covariance matrix (SCM, also called the cross-spectral matrix) reveal that the ordered noise eigenvalues of the SCM decay steadily, but common models predict equal noise eigenvalues. Random matrix theory (RMT) is used to derive and discuss properties of the eigenvalue spectrum of the data SCM for linear arrays, with an application to ocean acoustic data. Noise on the array is considered either incoherent or propagating acoustic noise that is coherent across the array. Using conventional three-dimensional or two-dimensional isotropic noise models with full or snapshot-deficient observations, realizations of the SCM eigenvalues are explained using RMT. Deep-water towed-array data are analyzed and it is shown that the eigenvalues of the SCM compare well with theory. It is demonstrated how RMT can be applied to study eigenvalue spectrum estimation as dependent on array properties (element spacing to wavelength ratio) and data sampling (snapshots). Apart from explaining the observed noise eigenvalue spectrum, the improved model of the eigenvalue spectrum has important applications in array signal processing.