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Characterizing turbulence profile layers through celestial single-source observations

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Abstract

Future spacecraft missions aim to communicate with the Earth using near-infrared lasers. The possible bit rate of free-space optical communication (FSOC) is orders of magnitude greater when compared to current radio frequency transmissions. The challenge of ground–space FSOC is that atmospheric turbulence perturbs optical wavefront propagation. These wavefront aberrations can be measured using a Shack–Hartmann wavefront sensor (SHWFS). A ground-based adaptive optics (AO) system can mitigate these aberrations along the optical path by translating wavefront measurements into deformable mirror commands. However, errors result from atmospheric turbulence continuously evolving, and there are unavoidable delays during AO wavefront correction. The length of an acceptable delay is referred to as the coherence time—a parameter dependent on the strength of turbulence profile layers and their corresponding wind-driven velocity. This study introduces a novel technique, to the best of our knowledge, for using SHWFS single-source observations, e.g., the downlink signal from a geostationary satellite, to measure the strength and velocity of turbulence profile layers. This work builds upon previous research and demonstrates that single-source observations can disentangle turbulence profile layers through studying the cross-covariance of temporally offset SHWFS centroid measurements. Simulated data are used to verify that the technique can recover the coherence time. The expected and measured results have a correlation coefficient of 0.95.

© 2022 Optical Society of America

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Corrections

10 January 2022: Typographical corrections were made to the body of the paper.


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Data Availability

All of the plots were rendered in Matplotlib [34]. The data analysis software was written in Python using the NumPy and SciPy libraries [35,36]. Other data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

34. J. D. Hunter, “Matplotlib: a 2D graphics environment,” Comput. Sci. Eng. 9, 90–95 (2007). [CrossRef]  

35. C. R. Harris, K. J. Millman, S. J. van der Walt, et al., “Array programming with NumPy,” Nature 585, 357–362 (2020). [CrossRef]  

36. P. Virtanen, R. Gommers, T. E. Oliphant, et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nat. Methods 17, 261–272 (2020). [CrossRef]  

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