Space situational awareness is often limited by the ability of sensors to produce resolved data on space objects. Large objects cannot be resolved with small low-cost telescopes; objects in geo-synchronous orbit are too remote to be resolved. The best hope of ending these limitations is to make it possible to determine important features of space objects from unresolved data, typically the temporal light curves that are produced by measuring only the integrated brightness of the space object as it passes overhead a sensor on the ground. Both supervised processing (i.e., pattern classification) and unsupervised processing (i.e., Kalman filter) of light curve data have shown success in extracting space object features. In this proposal we set forth a system that combines the merits of both supervised and unsupervised processing to more fully automate the exploitation of unresolved space object temporal light curve data.
Benefit: The successful results of this project will offer Space Situational Awareness (SSA) data from low-cost deployable telescopes that can travel to the world-wide locations where USA military assets operate and can benefit from the immediate response of the simpler SSA systems offered by this technology. The Potential Commercial Applications of this project extend to the construction and delivery, maintenance and upgrade of such worldwide assets, including delivery of workstations and full SSA packages, as well as cross-over applications in areas such as autonomous navigation.
Keywords: Space Situational Awareness (Ssa); Integrated Supervised And Unsupervised Processing (Isup); Multiple Model Adaptive Estimation (Mmae); Pattern Classification; Kalman Filters
