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.

通过消除传感器对象上存在的转移函数效应，该方法解决了盲反卷积和噪声二阶周期稳定（CS2）信号时间波形的估计的双重问题。该方法在各种信号类型、转移函数和信号噪声比（SNR）下表现出高精度，对信号或转移函数没有先验知识要求。此方法有潜力改进机器学习模型的训练，当需要对具有不同转移函数的相同系统的信号进行聚合时。

二阶周期平稳信号的估计与解卷积