Antiderivative antialiasing (ADAA) has emerged as a recent approach to reduce aliases for mathematically defined nonlinearities. In this study, ADAA is applied to simplified nonlinear Volterra modeling, which is a method for blackbox modeling of Hammerstein nonlinearities. Previously reported ADAA approaches contain a variable difference term in the denominator and therefore rely on a continuous piecewise function to prevent very small denominators. However, when applied to simplified Volterra models, this denominator term is eliminated, resulting in a polynomial function. This polynomial ADAA was tested against the standard approach of low-pass filtering the input to prevent aliasing. It was found that these two approaches perform comparably but that by combining them together, superior alias reduction can be achieved.
https://www.aes.org/e-lib/browse.cfm?elib=21882
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