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

SpaceWERX, in partnership with Space Systems Command's Space Sensing System Delta 84 and the OPIR Tap Lab, are seeking innovative solutions supporting the next generation of missile detection, warning, tracking, and reporting. The objective of this SBIR Phase I effort is to evaluate the feasibility of applying innovative data processing techniques including the use of Artificial Intelligence and Machine Learning (AI/ML) methodologies to address the evolving challenges in missile detection, warning, tracking, and reporting. The primary goal of this research and development initiative is to advance data fusion, sensor integration, and real-time processing methods that enhance the accuracy, responsiveness, and reliability of satellite-based missile warning systems. This includes addressing the data interoperability from multi-orbit (GEO, MEO, LEO) satellite constellations, optimizing on-orbit and ground-based processing frameworks, and improving data quality from diverse sensing modalities from legacy and future Overhead Persistent Infrared (OPIR) space architectures. Specifically, this effort will focus on researching and developing state-of-the-art algorithms and processing techniques that support rapid and precise multi-sensor fusion, robust target identification, and accurate real-time 3D tracking of traditional, hypersonic, and maneuverable missile threats. By leveraging novel techniques in data association, sensor fusion, and edge processing, the solution will aim to mitigate limitations presented by diverse sensor resolutions, data cadences, and observational geometries. The project will also investigate methods to increase computational efficiency for onboard satellite processing and consider robustness against noise in highly dynamic operational environments.

The full objective, description, Phase I, II and III requirements and evaluation criteria are in the official topic on DSIP.