Randall Alliss, Northrop Grumman; Heather Kiley, Northrop Grumman Corporation; Mary Ellen Craddock, NGC; Danny Felton, NGC; Michael Mason, NGC
Keywords: Atmospheric Mitigation, Laser communications, optical communications, Artificial intelligence, machine learning
Abstract:
Space based imaging often times wastes its onboard resources capturing pictures of the tops of clouds when ground targeting could be easily optimized with the use of atmospheric mitigation decision aids. The interest in the use of space based optical communications, also known as laser communications, is also similarly impacted by clouds and could benefit from similar atmospheric mitigation decision aids in order to optimize its link availability. Laser communications provide secure, high data rate transmission in the absence of strong atmospheric fading that includes cloud liquid water, ice and atmospheric aberrations produced by pancake layer density gradients. A multi-year campaign to understand the impacts of atmospherics on space imaging as well as laser communication signals is underway. This campaign includes quantifying the impacts of the atmosphere on atmospheric data links, and developing operational concepts for mitigating transmission losses due to clouds, turbulence, and aerosols.
Advanced atmospheric mitigation decision aids for characterizing and forecasting optical links and for optimizing resources for ground imaging is currently being developed. They include the development and deployment of state of the art instruments designed to measure properties of clouds, including transmission loss, at optical ground sites. An Infrared Cloud Imager and LIDAR ceilometer will together provide high temporal frequency characterization of clouds and cloud transmission in the local skydome. This data will provide an estimate of the atmospheric fading and will be used to make decisions on switching the link from one optical ground site to another. In addition to in situ measurements of clouds, remote sensing geostationary satellites will provide improved resolution of clouds in and near the site as well as regionally around each site in the optical network. Finally combined use of numerical prediction of atmospherics including cloud and optical turbulence fading and artificial intelligence are underway and will help to optimize ground target resourcing hours and days in advance. Combining local and regional characterization along with high fidelity numerical modeling and deep machine learning is resulting in an optimized atmospheric mitigation decision aid that maximizes availability. A demonstration of these revolutionary and exciting activities will be presented at the conference.
Date of Conference: September 11-14, 2018
Track: Poster