A deep machine learning-based electro-optics system (TurbNet sensor) was developed to measure atmospheric turbulence refractive index structure parameter (C2n) at a high temporal resolution by processing short-exposure intensity scintillation patterns. The TurbNet sensor was composed of a remotely located LED beacon, an optical receiver telescope with a CCD camera for capturing short exposure pupil-plane intensity scintillation patterns, and a Jetson Xavier Nx embedded AIcomputing platform to implement the deep neural network (DNN)-based processing of LED beam scintillation images. Performance of the TurbNet sensor was evaluated over a 7 km atmospheric propagation path.
An electro-optics system (TurbNet sensor) composed of a remotely located laser beacon, optical receiver telescope with a CCD camera capturing short-exposure intensity scintillation patterns, and deep neural network (DNN)-based processor implemented on Jetson Xavier Nx embedded AI-computing platform was developed and utilized for real-time sensing of the atmospheric turbulence refractive index structure parameter (2Cn) over a 7 km propagation path at high temporal resolution.
Conference Committee Involvement (4)
Applications of Machine Learning 2023
23 August 2023 | San Diego, California, United States
Algorithms for Synthetic Aperture Radar Imagery XXX
2 May 2023 | Orlando, Florida, United States
AI and Optical Data Sciences IV
30 January 2023 | San Francisco, California, United States
Applications of Machine Learning 2022
23 August 2022 | San Diego, California, United States
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