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Example of a 600 × 800 pixel data frame compared to a 572 × 572 pixel cropped frame with the background subtracted, mask applied, and auto-focused. Blurred subapertures are also masked. Red 13 × 13 pixel boxes represent the tracking windows.
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Atmospheric turbulence is an inevitable source of wavefront distortion in all fields of long range laser propagation and sensing. However, the distorting effects of turbulence can be corrected using wavefront sensors contained in adaptive optics systems. Such systems also provide deeper insight into surface layer turbulence, which is not well under...
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Citations
... Manuscript received strength is useful for knowing the nature of atmospheric turbulence and mitigating the effects of atmospheric turbulence [11][12][13][14][15]. Some devices have been developed to estimate C 2 n , for example, scintillometers, sonicanemometers, and Hartmann Turbulence Sensor [16,17], etc. However, these optical devices can be bulky and costly. ...
Laser beam transmission in atmospheric turbulence causes image distortion and affects the quality of information transmission in the field of optical communication. The strength of the atmospheric turbulence, which can be characterized by refractive index structure constant
$C^2_n$
, significantly influences the properties of a laser beam. The accurate estimation of
$C^2_n$
is essential for understanding the strength of turbulence. Although multilayer perceptron (MLP) and deep neural network (DNN) has been applied to estimate the atmospheric turbulence strength, the estimation accuracy is sensitive to the strength of the turbulence. In this paper, we propose a method based on the convolution neural network (CNN) approach to estimate
$C^2_n$
ranging from 10−17to 10−13m−2/3. We experimentally demonstrate that the correlation coefficient (R
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
) of the model is 99.39%. The mean relative error (MRE), root mean square error (RMSE), and mean absolute error (MAE) are 0.0047, 0.0916, and0.0684, respectively. For the turbulence strength with the same order of refractive index structure constant
$C^2_n$
, the estimation accuracy of the weak turbulence is higher than that of medium and strong turbulence. Moreover, the mix training different levels of turbulence strength improves the estimation accuracy of
$C^2_n$
compared to that with the same order of
$C^2_n$
. Based on the high estimation accuracy of the CNN in the scheme, the proposed method will be able to provide a way of estimating the strength of atmospheric turbulence without the need for additional optical devices.
... Retrieve the turbulence by solving the inverse problem Based on Eq. (9), the C 2 n information in different turbulence regions is retrieved by solving an optimization problem to minimize the relative residual norms 12 . The solution X is determined by ...
... . . . The MATLAB-constrained nonlinear optimization function fmincon was used to find the minimum of Eq. (21) 12 . ...
Abstract Atmospheric turbulence can cause critical problems in many applications. To effectively avoid or mitigate turbulence, knowledge of turbulence strength at various distances could be of immense value. Due to light-matter interaction, optical beams can probe longitudinal turbulence changes. Unfortunately, previous approaches tended to be limited to relatively short distances or large transceivers. Here, we explore turbulence probing utilizing multiple sequentially transmitted longitudinally structured beams. Each beam is composed of Bessel-Gaussian ( $${{{{{{\rm{BG}}}}}}}_{{{{{{\mathcal{l}}}}}}{{=}}0,{k}_{z}}$$ BG l = 0 , k z ) modes with different $${k}_{z}$$ k z values such that a distance-varying beam width is produced, which results in a distance- and turbulence-dependent modal coupling to $${{{{{\mathcal{l}}}}}}{{{{{\mathscr{\ne }}}}}}0$$ l {{\relax \special {t4ht̂3)}\o:mathrel: {\unhbox \voidb@x \special {t4ht@+{38}{35}x2260;}x}}} 0 orders. Our simulation shows that this approach has relatively uniform and low errors (
