Data Mining Development for OCS/DCS SSA Operations

We propose developing model-based data mining tools that incorporate orbital dynamics with available data to assess the vulnerability of tactical satellites. We are proposing enhancement to predictive algorithms we developed for AFRL''s SAFIRE testbed under a FY07 SBIR. These tools take available data on the orbital catalog and missile models and propagate into the future to determine vulnerability windows of satellites to either a direct ascent launch or a satellite from another orbit; they are promising but additional data mining tools are required to fully populate their inputs. In the case of delta-V mapping, the estimated delta-V capability of a specific satellite is required to compute windows of opportunity from a map of delta-V over time. We propose an Unscented Kalman Filter as a ground tool to estimate satellite mass and maneuvers from ground observations, leading to better estimates of delta-V capability. The current direct ascent algorithms require knowledge of the launch site and a specific missile model. We propose developing a running forecast of possible threats considering a database of missile models and sites and additional and real-time dynamic analysis for discrimination between a benign launch and an attack. BENEFIT: All current and future DoD space missions could benefit from this technology. Using all available data to predict the vulnerability of our assets is a critical part of situation assessment. In this case we are providing both static predictions based on the satellite catalog and potential launches, and dynamic prediction for actual direct ascent launches. This technology is applicable to commercial missions from a safety point of view, although we are likely limited to US markets due to ITAR restrictions. The DAV tools can be used to assess risk from planned launches. The DV Mapping can be used for predicting collision risks or for assigning satellites from a service constellation to a particular rendezvous.