Abstract
A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric turbulence theoretical models and to evaluate them against the experimentally measured data.
Highlights
Sensing and characterization of atmospheric turbulence effects and analysis of their impact on laser beam and image propagation are deep-rooted in classical Kolmogorov theory [1,2]
A deep machine learning computational framework is applied for prediction of the atmospheric turbulence refractive index structure parameter C2n based on deep neural network (DNN) processing of short exposure images of turbulence-induced laser beam intensity scintillations
To support the DNN model development and machine learning experiments, several datasets composed of large numbers of instances were collected during several atmospheric measurement trials over a 7 km propagation path, and computer-generated using wave-optics numerical simulations to imitate the experimental trials
Summary
Sensing and characterization of atmospheric turbulence effects and analysis of their impact on laser beam and image propagation are deep-rooted in classical Kolmogorov theory [1,2]. The retrieval of C2n from measurements is based on analytical (or approximate) expressions that link C2n with the statistical characteristics of optical waves propagating in turbulence, which are derived from the classical turbulence theory This indirect, C2n sensing approach is widely used in conventional instruments such as optical scintillometers [7], differential image motion monitors (DIMMs) [8], Shack–Hartman sensors [9,10,11], etc. The legitimacy of this sensing concept is based on the Kolmogorov notion of atmospheric turbulence local statistical homogeneity and isotropy, and the ergodicity assumption implying that ensemble-averaged optical characteristics used in the
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