Profiling atmospheric turbulence using time-lapse imagery from two cameras

Santasri R. Bose-Pillai; Jack E. McCrae Jr.; Aaron J. Archibald; Christopher A. Rice; Steven T. Fiorino

doi:10.1117/12.2519022

14 May 2019 Profiling atmospheric turbulence using time-lapse imagery from two cameras

Santasri R. Bose-Pillai, Jack E. McCrae Jr., Aaron J. Archibald, Christopher A. Rice, Steven T. Fiorino

Proceedings Volume 11001, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXX; 1100112 (2019) https://doi.org/10.1117/12.2519022
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

For effective turbulence compensation, especially in highly anisoplanatic scenarios, it is useful to know the turbulence distribution along a path. Irradiance-based techniques suffer from saturation when profiling turbulence over long ranges and hence alternate techniques are currently being explored. In an earlier work, a method to estimate turbulence parameters such as path weighted Cⁿ₂and Fried’s coherence length r₀ from turbulence induced random, differential motion of extended features in the time-lapse imagery of a distant target was demonstrated. A technique to measure the distribution of turbulence along an experimental path using the time-lapse imagery of a target from multiple cameras is presented in this work. The approach uses an LED array as target and two cameras separated by a few feet at the other end of the path imaging the LED board. By measuring the variances of the difference in wavefront tilts sensed by a single camera and between the two cameras due to a pair of LEDs with varying separations, turbulence information along the path can be extracted. The mathematical framework is discussed and the technique has been applied on experimental data collected over a 600 m approximately horizontal path over grass. A potentially significant advantage of the method is that it is phase based, and hence can be applied over longer paths. The ultimate goal of this work is to profile turbulence remotely from a single site using targets of opportunity. Imaging elevated targets over slant paths will help in better understanding how turbulence varies with altitude in the surface layer.

Conference Presentation

Citation Download Citation

Santasri R. Bose-Pillai, Jack E. McCrae Jr., Aaron J. Archibald, Christopher A. Rice, and Steven T. Fiorino "Profiling atmospheric turbulence using time-lapse imagery from two cameras", Proc. SPIE 11001, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXX, 1100112 (14 May 2019); https://doi.org/10.1117/12.2519022

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