Understanding how atmospheric turbulence is distributed along a path helps in effective turbulence compensation and mitigation. Phase-based techniques to measure turbulence have potential advantages when used over long ranges since they do not suffer from saturation issues as the irradiance-based techniques. In an earlier work, we had demonstrated a method to extract turbulence information along a path using the time-lapse imagery of a LED array from a pair of spatially separated cameras. By measuring the differential motion of pairs of LEDs of varying separations, sensed by a single camera or between cameras, turbulence profiles could be obtained. However, by using just a pair of cameras, the entire path could not be profiled. By using multiple spatially separated cameras, improvements can be made on the profiling resolution as well as the fraction of the path over which profiling is possible. This idea has been demonstrated in the present work by using a camera bank comprising of 5 identical cameras, looking at an arrangement of 10 nonuniformly spaced LEDs over a slant path. The differential tilt variances measured at a single camera and between all pairs of cameras have been used to obtain turbulence information. Profiling thus with elevated targets will ultimately help in a better understanding of how turbulence varies with altitude in the surface layer.
Phase-based techniques to measure atmospheric turbulence have potential advantages when used over long ranges since they do not suffer from saturation issues as the irradiance-based techniques. The present work uses time-lapse imagery of a non-cooperative target from two spatially separated cameras to extract turbulence distribution along a path. By measuring the differential motion of pairs of extended features on the target, sensed by a single camera or between cameras, turbulence profiles can be obtained. Tracking the motion of extended features rather than point features allows estimation over a longer range. The approach uses a derived set of path weighting functions for differential tilt variances. The mathematical framework is discussed and the technique is applied to images collected of a multi-story building. Turbulence profiles over different slant paths are extracted from features at multiple levels of the building. This work will ultimately help in a better understanding of how turbulence varies with altitude in the surface layer.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.