PlumeSight: A machine learning based plume identification and classification method for LIDAR instrument data

LIDAR is a powerful tool for the measurement and characterization of aerosols in the atmosphere. In battlefield environments, a wide variety of aerosol constituents and plume types are present. Traditional LIDAR analysis methods aim to characterize plumes in well controlled environments with a minimum of atmospheric clutter. To extend the abilities of plume characterization and identification in cluttered environments, Michigan Aerospace Corporation proposes to combine its long history of experience in atmospheric LIDAR with its machine learning algorithm development capabilities to generate a robust and powerful real-time tool for use in the battlefield. This tool will provide real time analysis and classification of plume types as measured with atmospheric LIDAR units being operated in the field. Initial results using an in-house LIDAR demonstration system are encouraging; Phase I work will use government-furnished data as indicated in the topic description. The proposed work will allow us to validate measurement performance in more complex and relevant environments.