Estimation and Deconvolution of Second Order Cyclostationary Signals
This method addresses signal processing challenges in scenarios where signals from identical systems with different transfer functions need aggregation, potentially improving machine learning training, but it is incremental as it builds on existing cyclostationary signal theory.
The paper tackles the problem of blind deconvolution and estimation of noisy second-order cyclostationary signals affected by a transfer function, proving the existence of a deconvolution filter and demonstrating high precision in simulations across various conditions.
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.