Acquiring reflective polarization from arbitrary multi-layer surface based on Monte Carlo simulation.

Area of Science:

• Optics and Photonics
• Computational Physics
• Materials Science

Background:

• Understanding light interaction with complex surfaces is crucial for remote sensing and material characterization.
• Existing models often struggle with arbitrary layer structures and surface roughness.
• Polarization properties provide rich information about surface composition and topography.

Purpose of the Study:

• To develop a novel Monte Carlo model for simulating reflective polarization from multilayer rough surfaces.
• To validate the model's accuracy against experimental data and established analytical models.
• To investigate the influence of various parameters on reflective polarization.

Main Methods:

• Utilizing the micro-facets theory to model surface geometry and local normal vectors.
• Implementing a ray-tracing approach to simulate light propagation within multilayer media.
• Randomly sampling normal vectors based on layer-specific distributions.

Main Results:

• The proposed Monte Carlo model accurately simulates reflective polarization from multilayer rough surfaces.
• Simulated results show excellent agreement with reported measured data and SCATMECH analytical models.
• The model demonstrates correctness and effectiveness for complex surface interactions.

Conclusions:

• The developed Monte Carlo model provides a robust tool for analyzing reflective polarization.
• The model's findings offer guidance for applications such as target detection and material analysis.
• Further analysis revealed the impact of layer number, surface geometry, wavelength, and incident polarization on reflective polarization.