Machine Learning Enabled Prediction of Atmospheric Optical Turbulence From No-Reference Imaging
Abstract
Laser based communication and weapons systems are integral to maintaining the operational readiness and dominance of our Navy. Perhaps one of the most intransigent obstacles for such systems is the atmosphere. As the beams travel through the atmosphere there is loss of irradiance on target, beam spread, beam wander, and intensity fluctuations of the propagating laser beam. The refractive index structure parameter, C2n, is a measure of the intensity of the optical turbulence along a path. If C2n can be easily and efficiently determined in an operating environment, the prediction of laser performance will be greatly enhanced. The goal of this research is to use image quality features in combination with machine learning techniques to accurately predict the refractive index structure parameter, C2n. The models of particular interest to this research are the Generalized Linear Model, the Bagged Decision Tree, the Boosted Decision Tree, as well as the Random Forest Model. While the quantity of available training data had a significant impact on model performance, the findings indicate that image quality can be used to assist in the prediction of C2n, and that the machine learning models outperform the linear model.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 16, 2022
- Accession Number
- AD1171871
Entities
People
- Sklyer P. Schork
Organizations
- United States Naval Academy