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Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices

Signal Processing 2024-01-19 v1
Authors: Mohammad Farsi , Christian Häger , Magnus Karlsson , Erik Agrell
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Abstract

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.

Keywords

image reconstruction parameter estimation signal processing

Cite

@article{arxiv.2401.09917,
  title  = {Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices},
  author = {Mohammad Farsi and Christian Häger and Magnus Karlsson and Erik Agrell},
  journal= {arXiv preprint arXiv:2401.09917},
  year   = {2024}
}

Comments

Will be appeared in OFC 2024

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