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

Abstract

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.