Measuring atmospheric turbulence along folded paths using a laser-illuminated differential image motion monitor

Hanna, Randall T.; Brown, David M.; Brown, Andrea M.; Baldwin, Kevin

doi:10.1364/ao.468597

Cited by 2 publications

(1 citation statement)

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“…Many methods have been developed which make use of such direct optical measurements of distorted intensity and phase profiles of the propagating beam to infer atmospheric turbulence characteristics [26]. Some of the well known methods are the traditional optical scintillometer [27][28][29], differential image motion monitors [30], Shack-Hartmann wavefront sensors [31][32][33], light detection and ranging, scintillation detection and ranging (SCIDAR), Path-Resolved Optical Profilers, radiosonde data [34] and differential tilt variance measurement systems [35]. The vertical C 2 n profile has been measured using SCIDAR [36], multiaperture scintillation sensor [37], slope detection and ranging [38,39], monitor of wavefront outer scale profiles [40] and profiler of Moon limb [41], etc.…”

Section: Introductionmentioning

confidence: 99%

Real time characterization of atmospheric turbulence using speckle texture

Lochab,

Kumar,

Ghai

et al. 2023

J. Opt.

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A new method is proposed for the characterization of the atmospheric turbulence strength parameter ( C n 2 ) based on the principal component analysis (PCA) of the texture of the speckle intensity pattern obtained on long-range propagation of a focused charge +1 vortex beam. Employing the split-step propagation method, datasets containing instantaneous intensity images of the focused vortex beam are generated for three distinct C n 2 values, specifically 1 × 10 − 14 m − 2 / 3 , 5 × 10 − 14 m − 2 / 3 , and 1 × 10 − 13 m − 2 / 3 , representing medium to high turbulence levels for a 2 km propagation distance. The gray level co-occurrence matrix (GLCM) methodology is employed to extract key texture attributes, like contrast, correlation, homogeneity, and energy from the intensity images. These extracted texture parameters serve as inputs for training a PCA model, enabling the identification of associated C n 2 values. The PCA analysis exhibits distinct clustering of the first three principal components for each of the three C n 2 values, forming individual clusters on the PCA plot. Standard deviational ellipses are drawn to clearly demarcate these clusters on the PCA plot. The texture-based PCA classification of atmospheric turbulence was also performed for a focused Gaussian beam. The comparison of PCA plots between vortex and Gaussian beams showed that a pronounced clear separation of C n 2 values is obtained for the vortex beam. This indicates that the non-zero orbital angular momentum of the vortex beam also plays an important role in achieving the distinct separation of C n 2 values on the PCA plot. The proposed method can provide efficient real-time turbulence estimation solely on the basis of texture of the instantaneous intensity speckle with prior training and therefore may simplify the estimation of turbulence strength parameter.

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