Abstract
During the past few years there has been an increasing interest in applying higher-order statistics, namely cumulants, and their associated Fourier transforms, polyspectra, to a wide range of signal processing and system theory problems. Cumulants and polyspectra can make a big difference in those problems where signals are non-Gaussian and systems are nonminimum phase (or, nonlinear). This paper provides a brief overview of much of the work that has occurred when parametric models are used in conjunction with higher-order statistics. It covers: identification of MA processes, identification of AR processes, identification of ARMA processes, order determination, calculation of cumulants, calculation of polyspectra, extensions to multi-channel and two-dimensional systems, and applications.
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