Year: 2022
Type: preprint
Abstract: Atmospheric turbulence strength (Cn2 parameter) sensing based on processing of intensity scintillation patterns with deep neural network (DNN) is considered. It is shown that DNN re-training with propagation distance change can be avoided by scaling of Cn2 values obtained using a DNN trained for a nominal distance L0 . The required Cn2 scaling factor can be obtained using either an analytical expression derived from the Kolmogorov turbulence theory (theory-based scaling), or through wave-optics numerical modeling and simulations (M&S-based scaling).
Source: arXiv (Cornell University)
Authors: Filimonov, G. A., Vorontsov, M. A.
Institution: University of Dayton
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4303449574