We consider the problem of frequency domain kernel estimation using random multi-tone (harmonic) excitation for 2nd-order Volterra models. The basic approach is based on least squares minimization of model output error, and results for the Volterra kernel estimations with random multi-tone inputs and random Gaussian input are compared. We show that kernel estimation with multi-tones are very accurate and efficient compared to the latter. As an illustration, the proposed method is applied to a discrete input-output system obtained from the numerical simulation of a representative hydrodynamic system for modeling semiconductor device transport. We also consider the effect of noise in the kernel estimation.
Bicken, G., Carey, G. F., & Stearman, R. O. (2002). Frequency domain Kernel estimation for 2nd-order volterra models using random multi-tone excitation. VLSI Design, 15(4), 701–713. https://doi.org/10.1080/106551402100012318