Measured statistics of retroreflected 1064 nm laser light in atmospheric turbulence

The scintillation measured over close-to-ground retro-reflector links can be substantially enhanced due to the correlations experienced by both the direct and reflected echo beams. Experiments were carried out at China Lake, California, over a variety of ranges. The emphasis in this paper is on presenting the data from the 1.1 km retro-reflecting link that was operated for four consecutive days. The dependence of the measured irradiance flux variance on the solar fluence and on the temperature gradient above the ground is presented. The data are consistent with scintillation minima near sunrise and sunset, rising rapidly during the day and saturating at irradiance flux variances of ~10. Measured irradiance probability distributions of the retro-reflected beam are compared with standard probability density functions. The ratio of the irradiance flux variances on the retro-reflected to the direct, single-pass case is investigated with two data sets, one from a monostatic system and the other using an off-axis receiver system.

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Turbulence strength C n2 estimation from video using physics-based deep learning

Saha¹,

SALCIN²,

Kim³

et al. 2022

Opt. Express

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Images captured from a long distance suffer from dynamic image distortion due to turbulent flow of air cells with random temperatures, and thus refractive indices. This phenomenon, known as image dancing, is commonly characterized by its refractive-index structure constant C n 2 as a measure of the turbulence strength. For many applications such as atmospheric forecast model, long-range/astronomy imaging, and aviation safety, optical communication technology, C n 2 estimation is critical for accurately sensing the turbulent environment. Previous methods for C n 2 estimation include estimation from meteorological data (temperature, relative humidity, wind shear, etc.) for single-point measurements, two-ended pathlength measurements from optical scintillometer for path-averaged C n 2 , and more recently estimating C n 2 from passive video cameras for low cost and hardware complexity. In this paper, we present a comparative analysis of classical image gradient methods for C n 2 estimation and modern deep learning-based methods leveraging convolutional neural networks. To enable this, we collect a dataset of video capture along with reference scintillometer measurements for ground truth, and we release this unique dataset to the scientific community. We observe that deep learning methods can achieve higher accuracy when trained on similar data, but suffer from generalization errors to other, unseen imagery as compared to classical methods. To overcome this trade-off, we present a novel physics-based network architecture that combines learned convolutional layers with a differentiable image gradient method that maintains high accuracy while being generalizable across image datasets.

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Measured statistics of retroreflected 1064 nm laser light in atmospheric turbulence

Cited by 4 publications

References 29 publications

Optics detection using a dual channel approach

Optics detection using a dual channel approach

Atmospheric turbulence effects measured along horizontal-path optical retro-reflector links

Turbulence strength C n2 estimation from video using physics-based deep learning

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