Machine learning for optical turbulence prediction in geographically similar regions

Bethany N. Campbell; Kevin McBryde; Erich Walter; Kyle Drexler

doi:10.1117/12.2681468

3 October 2023 Machine learning for optical turbulence prediction in geographically similar regions

Bethany N. Campbell, Kevin McBryde, Erich Walter, Kyle Drexler

Proceedings Volume 12691, Laser Communication and Propagation through the Atmosphere and Oceans XII; 126910X (2023) https://doi.org/10.1117/12.2681468
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

The refractive index structure parameter (C_n²) is of interest because it characterizes turbulence, which affects optical propagation through the atmosphere, including free space optical communications, laser propagation, and imaging. This work seeks to develop a geography-agnostic model that can predict C_n² and received signal strength index (RSSI), with as few input parameters as possible. This work trains models including the Gaussian process regression, neural network, and bagged decision tree types, and use r-squared and root-mean squared error to compare model performance. Most of the data used to train and test the algorithms is collected in San Diego, a Csa-type climate (hot-summer Mediterranean climate) according to Köppen climate classification. We then demonstrate application of the trained models to a different site with similar climate, using available common input parameters, and quantitatively assess each model's respective efficacy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bethany N. Campbell, Kevin McBryde, Erich Walter, and Kyle Drexler "Machine learning for optical turbulence prediction in geographically similar regions", Proc. SPIE 12691, Laser Communication and Propagation through the Atmosphere and Oceans XII, 126910X (3 October 2023); https://doi.org/10.1117/12.2681468

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