Electro-Optical Sensors for Atmospheric Turbulence Strength Characterization with Embedded Edge AI Processing of Scintillation Patterns

This study introduces electro-optical (EO) sensors (TurbNet sensors) that utilize a remote laser beacon (either coherent or incoherent) and an optical receiver with CCD camera and embedded edge AI computer (Jetson Xavier Nx) for in situ evaluation of the path-averaged atmospheric turbulence refracti...

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Bibliographic Details
Published in:	Photonics Vol. 9; no. 11; p. 789
Main Authors:	Polnau, Ernst, Hettiarachchi, Don L. N., Vorontsov, Mikhail A.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-11-2022
Subjects:	Aperture Artificial neural networks Atmospheric models Atmospheric turbulence Cameras CCD cameras Datasets deep neural network electro-optics sensor embedded edge AI computing Experiments Inference Lasers Light Light intensity Luminous intensity Neural networks NVIDIA Jetson Xavier Nx Optical measuring instruments Optical receivers Optics Parameter estimation Performance evaluation Propagation real-time sensing Refractivity Remote sensors Scintillation Scintillation counters Sensors Signal processing Telescopes Turbulence
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Summary:	This study introduces electro-optical (EO) sensors (TurbNet sensors) that utilize a remote laser beacon (either coherent or incoherent) and an optical receiver with CCD camera and embedded edge AI computer (Jetson Xavier Nx) for in situ evaluation of the path-averaged atmospheric turbulence refractive index structure parameter Cn2 at a high temporal rate. Evaluation of Cn2 values was performed using deep neural network (DNN)-based real-time processing of short-exposure laser-beacon light intensity scintillation patterns (images) captured by a TurbNet sensor optical receiver. Several pre-trained DNN models were loaded onto the AI computer and used for TurbNet sensor performance evaluation in a set of atmospheric propagation inference trials under diverse turbulence and meteorological conditions. DNN model training, validation, and testing were performed using datasets comprised of a large number of instances of scintillation frames and corresponding reference (“true”) Cn2 values that were measured side-by-side with a commercial scintillometer (BLS 2000). Generation of datasets and inference trials was performed at the University of Dayton’s (UD) 7-km atmospheric propagation test range. The results demonstrated a 70–90% correlation between Cn2 values obtained with the TurbNet sensors and those measured side-by-side with the scintillometer.
ISSN:	2304-6732 2304-6732
DOI:	10.3390/photonics9110789