The study focuses on solving the dual problem of blind deconvolution and estimation of time waveform for noisy second-order cyclo-stationary signals. It introduces a blind method that eliminates transfer function effects from signals with varying statistics over time. The research aims to improve machine learning model training by aggregating signals from identical systems with different transfer functions. Various applications in telecommunications, radar, mechanics, and more are discussed. The paper outlines related works in the field, defines the problem statement, methodology for deconvolution filter estimation, and CS2 envelope estimation algorithm. Simulations demonstrate robustness under different parameters like TF poles, cyclic components in signals, and noise levels. Error analysis highlights parameter choices and method limitations while concluding with future research directions.
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by Igor Makienk... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.19290.pdfDeeper Inquiries