Single-ended in-situ atmospheric turbulence strength characterization using deep neural networks.

Single-ended in-situ atmospheric turbulence strength characterization using deep neural networks.

Presenter(s)

Prabjeet Saggu

Comments

11:20-11:40, Kennedy Union 311

Description

In Free Space Optical (FSO) communication systems, precise characterization of atmospheric turbulence strength is essential for propagation systems. This study investigates the use of Deep Neural Networks (DNNs) to evaluate atmospheric turbulence strength by analyzing scintillation patterns observed in double-pass laser beam propagation scenarios. Objective of this project to develop a DNN-based sensing data processing model capable of predicting the strength of atmospheric turbulence (��_��^2​) from simulated scintillation patterns in two distinct scenarios: single pass propagation and double pass propagation systems.

Publication Date

4-23-2025

Project Designation

Graduate Research

Primary Advisor

Andrew M. Sarangan, Mikhail A. Vorontsov

Primary Advisor's Department

Electro-Optics and Photonics

Keywords

Stander Symposium, School of Engineering

Institutional Learning Goals

Scholarship; Community

Single-ended in-situ atmospheric turbulence strength characterization using deep neural networks.

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