Abstract
Space traffic management is difficult. Monitoring and predicting the
accurate, real-time position of satellites for collision avoidance in
low earth orbit is an engineering challenge. The dominant source of
error in satellite prediction tracking models is in the determination of
space weather driven atmospheric drag. The High Accuracy Satellite Drag
Model (HASDM) (Storz et al., 2005) was developed by the U.S. Air Force
Space Command (AFSPC) between 2000–2005 to help solve this problem. It
is a data assimilative modeling system using the JB2008 thermospheric
density model (Bowman et al., 2008) plus continuously derived densities
from several dozens of calibration satellites to achieve <5%
density uncertainty at most epochs. The HASDM data is being made
available to the community of scientists and operators for the first
time. Under authority from the AFSPC, Space Environment Technologies
(SET) has extracted two solar cycles of operational High Accuracy
Satellite Drag Model (HASDM) data for scientific use and this is called
the SET HASDM database. Navigating and extracting information from this
database quickly and efficiently to manage satellite space traffic is
currently complex and tedious. We present the development of a
data-driven model for the HASDM mass density using Machine Learning and
an attempt to quantify the associated uncertainties.