As space operations become ever more complex and disaggregated and heterogenous space observation data sources proliferate, it is increasingly difficult to separate significant events from routine spaceflight activities and deliver actionable space domain awareness (SDA) to satellite and other operators. AI-based techniques offer the combination of scalability and near-real-time performance to support human-machine teaming paradigms necessary to enable proactive SDA in this increasingly congested, contested, and competitive space. The SDA project leverages AI-based technologies to improve both space domain representation and understanding. Additionally, new AI approaches will be developed to optimize the behaviors of sensors and translate AI reasoning or recommendations into human-interpretable forms. The team also plans to host public challenges intended to render these problems accessible to the SDA expert community and to AI experts outside the space community to accelerate the infusion of the latest AI developments into the project’s approaches. The goal is to provide a common framework and benchmark scenario that enables the comparison of the performances of various AI methods across a set of key SDA-specific problems. A common challenge task framework is also expected to accelerate steps towards the infusion of best-in-breed AI techniques into operational systems by providing a common reference implementation architecture for adaptation.
Team Linares
– Richard Linares (MIT PI)
– Jonathan How (MIT Co-PI),
– Suvendra Dutta (MIT Lincoln Laboratory Lead)
– Morgan Mitchell (DAF Liaison)