A Deep Machine Learning Algorithm to Optimize the Forecast of Atmospherics

Alexandria M. Russell, Northrop Grumman Mission Systems, Randall J. Alliss, Northrop Grumman Mission Systems, Billy D. Felton, Northrop Grumman Mission Systems

Keywords: Machine Learning, Atmospherics, Imaging, Transmission forecasts

Abstract:

Space-based applications from imaging to optical communications are significantly impacted by the atmosphere. Specifically, the occurrence of clouds and optical turbulence can determine whether a mission is a success or a failure. In the case of space-based imaging applications, clouds produce atmospheric transmission losses that can make it impossible for an electro-optical platform to image its target. Hence, accurate predictions of negative atmospheric effects are a high priority in order to facilitate the efficient scheduling of resources.

Date of Conference: September 19-22, 2017

Track: Poster

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