AI/ML for Next Generation of Missile Detection, Warning, Tracking, and Reporting

The purpose of this topic is to address a critical need within the U.S. Space Force’s Missile Warning, Tracking, and Response mission by advancing the application of artificial intelligence (AI) and machine learning (ML) technologies to improve multi-sensor fusion, missile tracking precision, and real-time situational awareness within a multi-orbit satellite framework (GEO, MEO, and LEO). Current technologies face challenges in fusing large volumes of disparate data across multiple sensing modalities, tackling latency in data processing, and accurately tracking increasingly sophisticated threats such as hypersonic and maneuverable missile systems. This effort seeks to develop and validate advanced data processing techniques including AI/ML solutions for both ground-based and satellite-based multisensor fusion capable of real-time processing for advanced OPIR Missile Detection, Warning, Tracking, and Reporting. These solutions will enable enhanced detection, tracking, and reporting of advanced missile threats by leveraging novel algorithms for target identification, data association, and real-time 3D tracking across multi-orbit platforms. Initial solutions should target feasibility studies and technical proof-of-concept demonstrations with the goal to transition the capability into the OPIR Tap Lab or similar testbed to build and test solutions in an operationally relevant environment. The advancements made on this effort have the potential to significantly contribute to the Space Force’s mission of persistent global surveillance, improved defensive decision-making, and sustained space superiority.