NASA SBIR awarded for machine learning thermosphere

NASA SBIR awarded for machine learning thermosphere

LOS ANGELES, CALIFORNIA – Space Environment Technologies announced today that its Machine learning Enabled Thermosphere Advanced by HASDM (META-HASDM) project was selected for funding by NASA 2020 Small Business Innovation Research (SBIR) program.
This project will make a previously inaccessible USAF density database publicly available as a benchmark and for use in predicting satellite drag. SET will partner with West Virginia University to provide a method to rapidly access state-of-art densities through machine learning (ML) algorithms, quantifying the uncertainty, and improving forecast ballistic coefficients.
The debris increase in Low Earth Orbit (LEO) poses a critical challenge for safely operating in LEO and calls for improved conjunction assessment (CA) strategies. To meet this challenge, a publicly accessible and economically viable way of accurately predicting drag on LEO objects is needed. The information content of the SET HASDM Database, condensed in a rapid ML algorithm, will enable prediction accuracy.
This proposal supports NASA’s mission to make advances in science, technology, and exploration for enhancing knowledge, education, innovation, economic vitality, and stewardship of the Earth. The proposed work is also highly relevant to the mission of NASA Conjunction Assessment Risk Analysis (CARA). This work will make space missions safer and improve NASA’s future capabilities as well as those of other government agencies and the aerospace industry. This increased safety applies to space traffic management, which will require much better thermospheric densities that affect satellite drag.
According to NASA, 409 technology proposals were selected, providing approximately $51 million to 312 small businesses in 44 states and Washington DC.

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