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 paper explores the fundamental phenomenology of weather-driven diurnal and nocturnal optical turbulence trends. Examining long duration persistent atmospheric measurements at Townes Institute Science and Technology Experimentation Facility (TISTEF), an outdoor laser range operated by the University of Central Florida (UCF), reveals key correlations between observed meteorological quantities and optical turbulence strength. A distributed set of meteorological instruments provide information on local conditions via temperature, pressure, relative humidity, net radiation, wind anemometers, cloud ceilometer, and a sky imager. The strength of optical turbulence is captured via a boundary layer scintillometer (BLS) and the delayed tilt anisoplanatism (DELTA) sensors. The paper compares the turbulence measurements against the performance of a physical weather-driven turbulence model and a deductive machine learning (ML) based turbulence model. These models attempt to accurately capture the relationship and phenomenology between meteorological conditions and optical turbulence. Additionally, the paper discusses an instrument concept that could augment current turbulence forecasting techniques to have improved short term forecasts.
The objective of this study is to explore the feasibility and accuracy of image subtraction for estimating optical turbulence. The proposed approach involves creating a differential image by subtracting consecutive recorded frames. Post processing techniques are applied to the differential image, allowing temporal changes caused directly by turbulence to be identified. Image subtraction was implemented in python and evaluated against traditional turbulence instruments such as a Scintec BLS2000 and a MZA DELTA.
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.
Experiments were conducted at the TISTEF laser range to evaluate the atmospheric turbulence data of several instruments. A Scintec BLS900, BLS2000, SLS20, MZA DELTA, and an Applied Technologies SATI-3A sonic anemometer were deployed on the 1 kilometer range and recorded measurements over a multi-day period. The data was then processed to compare the calculated refractive index structure parameter (C2n ) between the instruments. The BLS2000 and DELTA were also deployed to record turbulence measurements along a 13.5 kilometer slant path from the TISTEF site to the Vehicle Assembly Building roof on Kennedy Space Center property for additional evaluation.
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