A separable cross-entropy approach to power spectral estimation

An approach to power spectrum estimation that is based on a separable cross-entropy modeling procedure is presented. The authors start with a model of a multichannel, multidimensional, stationary Gaussian random process that is sampled on a nonuniform grid. An approximate separable model in which selected frequency samples of the process are modeled as independent random variables, is then fitted to it. Two cross-entropy-like criteria are used to select optimal separable approximations. One of them yields a spectral estimation algorithm that is a generalized version of Capon's maximum-likelihood method, and the other is similar to classical windowing methods. They discuss different strategies for designing bandpass filters for use with the cross-entropy approach.>