Space Situation Awareness (SSA). SSA is the ability to detect, track and characterize passive and active Resident Space Objects (RSOs). In light of the large number of RSOs (>20,000), and the generally accepted notion that our knowledge about the number and nature of most of the objects is severely limited, an unmet and urgent need exists for accurate tracking and characterization of RSOs. The need to maintain the uncertainty associated with tracked orbit state and RSO characterization within some target tolerance stipulates the number and frequency of observations required for each RSO, which in-turn drives the sensor management process.
To enhance the operational capabilities of the Space Surveillance Network (SSN) and realize its next generation version for effective SSA, it is imperative to adapt the data association and resource management methods to automate the RSO tracking and characterization operations. The ability to scale the current system to account for the expected growth to an order of magnitude more RSOs is conditioned upon the use of next generation high performance computing methods and the incorporation of more diverse and evolving data types in the catalog. In this respect, our research efforts are concentrated on the development of scalable algorithms for accurate data association, track estimation, uncertainty quantification, and dynamic resource/catalog management elements to finally enable a user/analyst-driven composable framework for accurate RSO characterization and tracking.