A Machine Learning Model for Prediction of Optical Turbulence in Near-Maritime Environments
Abstract
As a beam propagates, it is subject to fluctuations in the refractive index of air. These effects can be modeled as optical turbulence. Optical turbulence limits the effectiveness of laser-based weapons and communication systems employed by the United States Navy. Models developed to predict optical turbulence through the structure constant Cn2 are sensitive to absolute air temperature. Existing models have, however, failed to accurately predict the rapid beam attenuation and corresponding high values of Cn2 observed in maritime and near maritime environments. In response, data-driven machine learning models were developed to predict the refractive index structure parameter Cn2, and to explore the importance of various environmental factors on its prediction. The current study uses 15 months of Cn2 field measurements collected along an 890 m scintillometer link over the Severn River at the United States Naval Academy. Measures of optical turbulence are complemented by corresponding measurements of 12 environmental parameters. Fully data-driven models were trained, developed, and tested to enhance Cn2 prediction accuracy in the near-maritime environment. Analysis of these models resulted in better understanding of the relative importance of each environmental parameter inaccurately predicting Cn2. To our knowledge, this is the first application of purely data-driven machine learning models for predicting Cn2 in the near-maritime environment.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 06, 2020
- Accession Number
- AD1136696
Entities
People
- Christopher D. Jellen
Organizations
- United States Naval Academy