Structured decomposition design of partial Mueller matrix polarimeters

Andrey S. Alenin, J Scott Tyo

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Partial Mueller matrix polarimeters (pMMPs) are active sensing instruments that probe a scattering process with a set of polarization states and analyze the scattered light with a second set of polarization states. Unlike conventional Mueller matrix polarimeters, pMMPs do not attempt to reconstruct the entire Mueller matrix. With proper choice of generator and analyzer states, a subset of the Mueller matrix space can be reconstructed with fewer measurements than that of the full Mueller matrix polarimeter. In this paper we consider the structure of the Mueller matrix and our ability to probe it using a reduced number of measurements. We develop analysis tools that allow us to relate the particular choice of generator and analyzer polarization states to the portion of Mueller matrix space that the instrument measures, as well as develop an optimization method that is based on balancing the signal-to-noise ratio of the resulting instrument with the ability of that instrument to accurately measure a particular set of desired polarization components with as few measurements as possible. In the process, we identify 10 classes of pMMP systems, for which the space coverage is immediately known. We demonstrate the theory with a numerical example that designs partial polarimeters for the task of monitoring the damage state of a material as presented earlier by Hoover and Tyo [Appl. Opt. 46, 8364 (2007)]. We show that we can reduce the polarimeter to making eight measurements while still covering the Mueller matrix subspace spanned by the objects.

Original languageEnglish (US)
Pages (from-to)1302-1312
Number of pages11
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume32
Issue number7
DOIs
StatePublished - 2015

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Computer Vision and Pattern Recognition

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