11 May 2018 Characterizing atmospheric turbulence over long paths using time-lapse imagery
Author Affiliations +
Abstract
In recent times, there has been a growing interest in measuring atmospheric turbulence over long paths. Irradiance based techniques such as scintillometry, suffer from saturation and hence commercial scintillometers have limited operational ranges. In the present work, a method to estimate path weighted Cn2 from turbulence induced random, differential motion of extended features in the time-lapse imagery of a distant target is presented. Since the method is phase based, it can be applied to longer paths. The method has an added advantage of remotely sensing turbulence without the need for deployment of sensors at the target location. The imaging approach uses a derived set of path weighting functions that drop to zero at both ends of the imaging path, the peak location depending on the size of the imaging aperture and the relative sizes and separations of the features whose motions are being tracked. For sub-aperture sized features and separations, the peaks of the weighting functions are closer to the target end of the path. For bigger features and separations, the peaks are closer to the camera end. Using different sized features separated by different amounts, a rich set of weighting functions can be obtained. These weighting functions can be linearly combined to produce a desired weighting function such as that of a scintillometer or that of r0. The time-lapse measurements can thus mimic the measurements of a scintillometer or any other instrument. The method is applied to both simulated and experimentally obtained imagery and some validation results with a scintillometer is shown as well.
Conference Presentation
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Santasri R. Bose-Pillai, Santasri R. Bose-Pillai, Jack E. McCrae, Jack E. McCrae, Ryan A. Wood, Ryan A. Wood, Connor E . Murphy, Connor E . Murphy, Christopher A. Rice, Christopher A. Rice, Steven T. Fiorino, Steven T. Fiorino, } "Characterizing atmospheric turbulence over long paths using time-lapse imagery", Proc. SPIE 10650, Long-Range Imaging III, 106500A (11 May 2018); doi: 10.1117/12.2305077; https://doi.org/10.1117/12.2305077
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