High temporal resolution sensing of atmospheric turbulence refractive index structure parameter (Cn^2) based on embedded edge AI-processing of scintillation images

Don L. N. Hettiarachchi; Ernst Polnau; Mikhail A. Vorontsov

doi:10.1117/12.2618431

27 May 2022 High temporal resolution sensing of atmospheric turbulence refractive index structure parameter (C_n²) based on embedded edge AI-processing of scintillation images

Don L. N. Hettiarachchi, Ernst Polnau, Mikhail A. Vorontsov

Author Affiliations +

Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 121020B (2022) https://doi.org/10.1117/12.2618431
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

A deep machine learning-based electro-optics system (TurbNet sensor) was developed to measure atmospheric turbulence refractive index structure parameter (C²_n) 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.

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Citation Download Citation

Don L. N. Hettiarachchi, Ernst Polnau, and Mikhail A. Vorontsov "High temporal resolution sensing of atmospheric turbulence refractive index structure parameter (C_n²) based on embedded edge AI-processing of scintillation images", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 121020B (27 May 2022); https://doi.org/10.1117/12.2618431

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