Estimation of atmospheric turbulence using differential motion of extended features in time-lapse imagery

Abstract

Accurate characterization of atmospheric turbulence is useful for performance assessment of optical systems operating in real environments and for designing systems to mitigate turbulence effects. Irradiancebased techniques such as scintillometry, suffer from saturation, and hence commercial scintillometers have limited operational ranges. A method to estimate turbulence parameters, such as path weighted C 2 n and Fried's coherence diameter r 0 from turbulence-induced random, differential motion of extended features in the timelapse imagery of a distant target is presented. Since the method is phase-based, it can be applied to longer paths. It has an added advantage of remotely sensing turbulence without the need for deployment of sensors at the target location. The 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. 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 approximate a desired weighting function such as that of a scintillometer or that of r 0 in inhomogeneous turbulence. The time-lapse measurements can thus mimic the measurements of a scintillometer or any other instrument. The method is applied to images captured along two different paths, and the estimates are compared to co-located scintillometer measurements. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

“…The other difference is that we employ the tilt statistics derived by Bose-Pillai. 48 These statistics have been shown to provide excellent agreement with the anisoplanatic simulator statistics. 2 It should be noted that the warping generated by our fast simulator is anisoplanatic, but the blurring is isoplanatic.…”

Application of tilt correlation statistics to anisoplanatic optical turbulence modeling and mitigation

“…This can be done in a fashion similar to that presented by Schwartzman et al [28]. However, here we use the tilt correlation statistics from Bose-Pillai et al [9,19] that have been validated with our anisoplanatic turbulence simulator [22].…”

“…Other methods are scene-based and use only the natural imagery acquired by an imaging sensor [14][15][16][17][18][19][20]. Methods that explicitly address camera or scene motion include [9,21].…”

“…There is a great deal of interest in characterizing atmospheric optical turbulence to help understand its impact on imaging and optical communications [6][7][8][9][10]. Thus, estimating the Fried parameter is important for many other applications in addition to the BMWF algorithm.…”

Estimating Turbulence Distribution over a Heterogeneous Path Using Time-Lapse Imagery from Dual Cameras

“…Gladysz et al have also used differential tilt measurements at a single receiver due to an array of LEDs to characterize turbulence along a horizontal path [5]. In an earlier work, a time-lapse imaging technique was introduced to measure integrated turbulence parameters remotely from a single site [6]. The technique was applied to images collected over a 7 km slant path from Fitz Hall at University of Dayton to Dayton VA Medical Center.…”