Citation: Polnau, E.; Hettiarachchi, D.L.N.; Vorontsov, M.A. Electro-Optical Sensors for Atmospheric Turbulence Strength Characterization with Embedded Edge AI Processing of Scintillation Patterns. Photonics 2022, 9, 789. https://doi.org/10.3390/ photonics9110789 Received: 31 August 2022 Accepted: 20 October 2022 Published: 24 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). photonics hv Article Electro-Optical Sensors for Atmospheric Turbulence Strength Characterization with Embedded Edge AI Processing of Scintillation Patterns Ernst Polnau 1 , Don L. N. Hettiarachchi 1 and Mikhail A. Vorontsov 1,2, * 1 Intelligent Optics Laboratory, School of Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USA 2 Optonica LLC, Spring Valley, OH 45370, USA * Correspondence: mvorontsov1@udayton.edu Abstract: 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 turbu- lence refractive index structure parameter C 2 n at a high temporal rate. Evaluation of C 2 n 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”) C 2 n 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 at- mospheric propagation test range. The results demonstrated a 70–90% correlation between C 2 n values obtained with the TurbNet sensors and those measured side-by-side with the scintillometer. Keywords: atmospheric turbulence; deep neural network; electro-optics sensor; embedded edge AI computing; NVIDIA Jetson Xavier Nx; real-time sensing 1. Introduction Performance of atmospheric electro-optical (EO) systems, such as free-space laser communication, remote sensing, active imaging, directed energy, and optical surveillance can be significantly degraded by atmospheric effects (e.g., optical turbulence, refractivity and absorption) [1–5]. Atmospheric turbulence causes the most detrimental impact on laser-beam and image characteristics, especially in the deep turbulence conditions typical for slant and/or extended-range propagation scenarios [6]. In contrast with refractivity and absorption, atmospheric turbulence strength, as characterized by the refractive index structure parameter C 2 n , can strongly fluctuate during only a few seconds for a stationary target [7,8] and by an order of magnitude for high-velocity targets when the line-of-site rapidly sweeps across a large volume of turbulence. To evaluate and mitigate the negative impact of atmospheric effects on the perfor- mance of EO systems, it is necessary for these effects to be accurately characterized and potentially forecast along the line-of-site to the target (including moving targets) at a tem- poral resolution that is significantly higher (in situ) than in today’s available atmospheric turbulence characterization EO sensors. In situ turbulence strength characterization can be applied for real-time parameter adjustment in wavefront sensing, beam control and adap- tive optics systems [5,9,10], for turbulence effects mitigation in atmospheric imaging [11], and to reduce the bit error rate in laser communication systems [12–14]. Conventional Photonics 2022, 9, 789. https://doi.org/10.3390/photonics9110789 https://www.mdpi.com/journal/photonics