Integrated Sensing Module for In-Situ Atmospheric Path Characterization & Forecasting with Deep Machine Learning

Low temporal resolution in turbulence characterization is one of the major bottlenecks for defense and commercial applications of laser weapons, laser communication, power beaming, and imaging surveillance on the defense side, and remote monitoring of climate change, evaluation of green-energy source impacts on the environment, air quality monitoring at airports, etc., on the commercial side. This Phase II effort will develop an integrated sensing suite prototype for atmospheric characterization and diagnostics (AC&D) based on deep machine learning-based signal processing and sensing data fusion. This prototype will be capable of in-situ turbulence, refractivity  and beam quality metric monitoring and ahead forecasting at high temporal rates, which is required for real time monitoring and mitigation of the negative impacts of atmospheric turbulence on the performance of advanced Air Force atmospheric electro-optics platforms.