MICHIGAN AEROSPACE CORP — Department of Defense SBIR Phase II: CBD202-002

Description

In battlefield environments, the atmosphere is littered with aerosols from a variety of sources: dust from vehicles and foot traffic; smoke from firearms and explosives; and, potentially, plumes of weaponized aerosols. In order to improve situational awareness, it is critical to develop a real-time knowledge of the presence, type, and structure of aerosol plumes on the battlefield. Elastic LIDAR systems are powerful remote sensing tools capable of detecting, quantifying, and classifying clouds of aerosols.LIDAR instruments have a long history of use in atmospheric remote sensing for making measurements of atmospheric aerosol types and concentrations. Beyond atmospheric science, LIDAR has use in the measurement and quantification of man-made aerosol releases ranging from smoke grenades to chemical explosives to anthrax.To extend the ability of LIDAR systems tocharacterize and classify plumes in cluttered environments, Michigan Aerospace Corporation proposes to further develop itsplumesight algorithm. Operating on raw LIDAR backscatter returns, the plumesightalgorithmreconstructs high fidelity images of the plumeusing machine learning algorithms, and then usesdeep convolutional recurrent networksto learn how plumes of differentsubstances evolve over time. By exploiting these complex spatiotemporal patterns, the plumesight algorithm is able to classify plumes using elastic LIDAR returns.