Estimation of atmospheric optical turbulence strength in realistic airborne environments

Abstract

In this paper, atmospheric optical turbulence strength is estimated for realistic airborne environments using a modified phase-variance approach, as well as a modified slope-discrepancy approach. Realistic airborne environments are generated using wave-optics simulations of a plane wave propagating through increasing strengths of homogeneous atmospheric optical turbulence, both with and without aero-optical contamination (from in-flight wavefront sensor data) and additive-measurement noise. In comparison to the modified phase-variance approach, the results show that the modified slope-discrepancy approach more accurately estimates atmospheric optical turbulence strength over a wide range of conditions. Such results are encouraging for realistic airborne environments because they can be scaled to different freestream conditions as long as the boundary layer is considered canonical.

Optical-turbulence characterization of a littoral test environment using a Shack-Hartmann wavefront sensor

“…[33][34][35] An aperture transmittance function of diameter, D = 25 cm, was applied to the phase screen and the resulting complex-optical field was used in a simulated SHWFS model. 5 The phase screens were created for a range of optical-turbulence conditions where C 2 n was varied from 4×10 −16 to 9×10 −13 m −2/3 . The "known" r 0 values were calculated using the phase-variance approach on the finely-resolved simulation phase screen.…”

“…Here, the average gradient of the incoming phase aberration over each subaperture in the pupil plane is estimated from irradiance-pattern deflections in the image plane. 5 Subsequently, these so-called slope estimates are traditionally used in a least-squares reconstructor to estimate the continuous Optical-Path Difference (OP D) aberration.…”

Characterization of underwater optical turbulence caused by natural convection generated by Rayleigh-Bénard using machine learning

“…In another study, atmospheric optical turbulence was estimated in a simulated environment. 9 The simulated environment was created through a MATLAB simulation that propagated a plane wave through increasing levels of optical turbulence. 9 The ability to estimate the atmospheric optical turbulence in a simulated environment is important because those techniques can be modified for use in different environmental conditions as well as in dynamic real-world experiments.…”

“…9 The simulated environment was created through a MATLAB simulation that propagated a plane wave through increasing levels of optical turbulence. 9 The ability to estimate the atmospheric optical turbulence in a simulated environment is important because those techniques can be modified for use in different environmental conditions as well as in dynamic real-world experiments. 9 This is relevant because the underwater environment for laser propagation being characterized using machine learning (ML) is a small-scale controlled environment.…”

“…9 The ability to estimate the atmospheric optical turbulence in a simulated environment is important because those techniques can be modified for use in different environmental conditions as well as in dynamic real-world experiments. 9 This is relevant because the underwater environment for laser propagation being characterized using machine learning (ML) is a small-scale controlled environment. The ability to characterize a controlled environment for laser propagation is an important step before being able to characterize an uncontrolled environment for laser propagation.…”