Frequency-domain Volterra kernel estimation via higher-order statistical signal processing

We discuss the utilization of higher-order spectral moments to determine frequency-domain Volterra kernels, given time series records of the random excitation and response of a nonlinear physical system. In particular, we consider frequency domain third-order Volterra kernel identification for nonGaussian excitation. Next an orthogonal Volterra like model valid for nonGaussian excitation is described. This model eliminates the interference terms associated with the nonorthogonal Volterra model, and thus facilitates decomposition of an observed power spectrum into its constituent linear quadratic, and cubic components