Predicting atmospheric turbulence (Cn2) with deep neural networks: leveraging laser beam phase and turbulence profiling

Victor A. Kulikov; Artem M. Vorontsov

doi:10.1117/12.3027226

7 October 2024 Predicting atmospheric turbulence (Cn2) with deep neural networks: leveraging laser beam phase and turbulence profiling

Victor A. Kulikov, Artem M. Vorontsov

Author Affiliations +

Proceedings Volume 13149, Unconventional Imaging, Sensing, and Adaptive Optics 2024; 131490Y (2024) https://doi.org/10.1117/12.3027226
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Recent studies have shown that incorporating phase information alongside registered intensity distribution enhances Deep Neural Networks (DNNs) based predictions of refractive index structure function, C_n², which characterizes turbulence strength. In this study, we conducted numerical simulations to analyze the impact of received phase information on C_n² recovery error depending on properties of the used sensor and atmospheric conditions. We examined the size and number of lenses required to form an intensity pattern in the focal plane and its impact on C_n² predictions. We also considered the impact of presence of localized turbulence layer on accuracy of DNN-based C_n² predictions, ways to eliminate this impact, and locate the layer. We used wave-optics numerical simulations to generate datasets of short-exposure intensity distributions. The atmospheric propagation of a collimated Gaussian beam over the considered distance was simulated using the split-step method. The atmospheric turbulence followed von Karman’s power spectrum and was represented by a set of phase screens. These datasets were then used for training, validating, and testing the DNN models. The DNN architecture consisted of feature extracting blocks each containing three convolutional and max-pooling layers, and a single perceptron layer with 20 neurons. The model featured 16 feature extracting blocks with identical topology and weights, simultaneously receiving sequential frames from the dataset.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Victor A. Kulikov and Artem M. Vorontsov "Predicting atmospheric turbulence (Cn2) with deep neural networks: leveraging laser beam phase and turbulence profiling", Proc. SPIE 13149, Unconventional Imaging, Sensing, and Adaptive Optics 2024, 131490Y (7 October 2024); https://doi.org/10.1117/12.3027226

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