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There are many statistical tools to extract information from random signals. They predominantly use first and second order statistics. However, in the presence of nonlinearity in systems, many signals cannot be analyzed adequately by second order statistical methods. For this reason, higher order statistical methods have been developed. These methods are very useful in problems where non-Gaussian, non-minimum phase, phase coupling or nonlinear behavior and robustness to additive noise are important. Detection and classification using higher order statistical and spectral techniques have been proposed for use in communication and pattern recognition. They have the potential to elicit better performance from sensors, sensor networks and channels with applications in coding, filtering and detection techniques. This paper provides an introduction to higher order spectra and reviews a number of these techniques

A Review of Higher Order Statistics and Spectra in Communication Systems