Papers › Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Mohammad Farsi, Christian Häger, Magnus Karlsson, Erik Agrell
We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resilience compared to previous inverse scattering methods, while highlighting challenges related to model overparameterization.
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