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Research Papers: Techniques and Procedures

Implementing Artificial Intelligence in Predicting Metrics for Characterizing Laser Propagation in Atmospheric Turbulence

[+] Author and Article Information
Diego Alberto Lozano Jimenez

Department of Mechanical Engineering,
The University of Texas at El Paso,
El Paso, TX 79968
e-mail: dalozano4@miners.utep.edu

V. M.Krushnarao Kotteda

Department of Mechanical Engineering,
The University of Texas at El Paso,
500 W. University Ave.,
El Paso, TX 79968
e-mail: vkotteda@utep.edu

Vinod Kumar

Department of Mechanical Engineering,
The University of Texas at El Paso,
El Paso, TX 79968
e-mail: vkumar@utep.edu

V. S. Rao Gudimetla

Directed Energy Directorate,
The Air Force Research Laboratory,
Kihei, HI 96753
e-mail: venkata.gudimetla@us.af.mil

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received August 28, 2018; final manuscript received May 1, 2019; published online June 3, 2019. Assoc. Editor: Meredith Metzer.

J. Fluids Eng 141(12), 121401 (Jun 03, 2019) (8 pages) Paper No: FE-18-1564; doi: 10.1115/1.4043706 History: Received August 28, 2018; Revised May 01, 2019

The effects of a laser beam propagating through atmospheric turbulence are investigated using the phase screen approach. Turbulence effects are modeled by the Kolmogorov description of the energy cascade theory, and outer scale effect is implemented by the von Kármán refractive power spectral density. In this study, we analyze a plane wave propagating through varying atmospheric horizontal paths. An important consideration for the laser beam propagation of long distances is the random variations in the refractive index due to atmospheric turbulence. To characterize the random behavior, statistical analysis of the phase data and related metrics are examined at the output signal. We train three different machine learning algorithms in tensorflow library with the data at varying propagation lengths, outer scale lengths, and levels of turbulence intensity to predict statistical parameters that describe the atmospheric turbulence effects on laser propagation. tensorflow is an interface for demonstrating machine learning algorithms and an implementation for executing such algorithms on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets to large-scale distributed systems and thousands of computational devices such as GPU cards. The library contains a wide variety of algorithms including training and inference algorithms for deep neural network models. Therefore, it has been used for deploying machine learning systems in many fields including speech recognition, computer vision, natural language processing, and text mining.

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Figures

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Fig. 1

tensorflow supervised algorithm flowchart

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Fig. 2

Description of Kolmogorov energy cascade theory

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Fig. 3

Schematic of laser propagation between Δz

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Fig. 4

Intensity, wrapped, and unwrapped phase screens using the matlab embedded code and 2D Goldstein code

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Fig. 5

Random forest algorithm summarized

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Fig. 6

Mean phase variance at a propagation length of 10 km

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Fig. 7

Mean phase variance at a propagation length of 11 km

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Fig. 8

Mean phase variance at a propagation length of 12 km

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Fig. 9

Phase variance values for MLR

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Fig. 10

Phase variance values for RF

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Fig. 11

Phase variance values for DNN

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Fig. 12

Comparison of phase variance values from MLR, by shuffling the training as well as the test dataset, with actual values

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Fig. 13

Comparison of phase variance values from RF, by shuffling the training as well as the test dataset, with actual values

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Fig. 14

Comparison of phase variance values from DNN, by shuffling the training as well as the test dataset, with actual values

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