Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness

Knowledge of all space objects in orbit and the space environment is collected and maintained by the Space Surveillance Network (SSN). This task is becoming more difficult as the number of objects currently tracked increases due to breakup events and improvements in sensor detection capabilities. The SSN is tasked with maintaining information on over 22,000 objects, 1,100 of which are active. In particular, low-Earth orbiting satellites are heavily influenced by atmospheric drag which is difficult to model due to fluctuations in the upper atmospheric density. These fluctuations are caused by variations in the Solar energy flux which heats Earth’s atmosphere causing it to expand. This research uses probabilistic models to characterize and account for the fluctuations in the Earth’s atmosphere. By correctly estimating the fluctuations, our work contributes to improving the ability to determine the likelihood of satellite collisions in space.

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