The strength of a machine learning algorithm is that given enough data and computation, any trend or pattern appearing within that data can be approximated without explicitly involving underlying understanding. The refractive index structure parameter, C2n, is a measure of the strength of fluctuations in the refractive index along a path and one of the most significant contributors to beam quality. Weather data including C2n is collected over a period of several months at the TISTEF (Townes Institute Science and Technology Experimentation Facility) laser range. Once data is aggregated, important features are selected and the data is processed. The AutoGluon framework is used for exploratory analysis of the effectiveness of neural network structures and Keras is used in final model building. The model chosen as a result of this process indicates that machine learning is able to generalize trends in the fluctuations of C2n over time.
This research summarizes the events of a laser propagation test at the TISTEF laser range during January 2024. A 1064 nanometer (nm) continuous-wave (cw) fiber laser was focused at 1 kilometer and propagated in a variety of conditions over a week-long period. Meteorological instruments including a Scintec BLS900, MZA DELTA, and sonic anemometers were deployed along the optical path. The propagated beam spot was recorded at 100 Hz from both transmit and receive site locations. The processed imagery from both cameras generated beam profile data such as short-term spot size, long-term spot size, and beam wander. These statistics were explored as a function of measured atmospheric parameters such as visibility, refractive index structure parameter, wind speed, and more.
This research paper discusses the application of several image-based techniques for measuring optical turbulence. University of Central Florida researchers have previously prototyped and fielded a differential disturbance tracker at the TISTEF 1 kilometer range. This effort has evolved into the development of a software suite that implements image processing techniques such as blob detection, centroid tracking, and optical flow for estimating the refractive index structure parameter. To validate each method, imagery was collected over the 1 kilometer path. The processed results were compared against measurements from an MZA DELTA system.
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