  This method solves the dual problem of blind deconvolution and estimation of
the time waveform of noisy second-order cyclo-stationary (CS2) signals that
traverse a Transfer Function (TF) en route to a sensor. We have proven that the
deconvolution filter exists and eliminates the TF effect from signals whose
statistics vary over time. This method is blind, meaning it does not require
prior knowledge about the signals or TF. Simulations demonstrate the algorithm
high precision across various signal types, TFs, and Signal-to-Noise Ratios
(SNRs). In this study, the CS2 signals family is restricted to the product of a
deterministic periodic function and white noise. Furthermore, this method has
the potential to improve the training of Machine Learning models where the
aggregation of signals from identical systems but with different TFs is
required.
