Spectral Representation for a Class of Non-Gaussian Processes

A new model is developed for non-Gaussian processes. The model is based on the spectral representation theorem for weakly stationary processes, can match the second moment properties and several higher order moments of any non-Gaussian process, and consists of a superposition of harmonics with uncorrelated but dependent random amplitudes. The calibration of the model to a target non-Gaussian process may require iterations. The It? formula is used to calculate higher-order moments of the proposed model. These moments can be used to tune the model such that it matches some of the higher-order moments of a target non-Gaussian process in addition to its second-moment properties. The proposed model is useful for both Monte Carlo simulation and analytical studies on the response of linear and nonlinear systems to non-Gaussian noise. Examples are presented to illustrate the use of the proposed model.