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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