We propose to develop algorithms to improve debris management for electro-optical/infrared sensors (EO/IR) within C2BMC. Our goal is to improve fire control solutions, increase the quality of communicated sensor information, and reduce communication demands. We characterize the pre-intercept debris field mathematically by expressing the debris fields viewed on the sensor?s focal plane as 2D ellipses using an algorithm from the Two Micron All Sky Survey (2MASS) research project. Objects of interest are found using a Bayesian belief network (BBN). Two Kalman filters will be used to track the change in the 2D cloud shape and its LOS from a single sensor. This information can compactly express the 2D EO/IR scene information and future uncertainty. We propose to combine 2D data from two or more passive EO/IR sensors to produce 3D tracks of the clouds and targets of interest and to track the 3D debris cloud shape. The models developed will be implemented and tested by our research institution partner, Purdue University Center for Integrated Systems in Aerospace, in their ARchitecture Testing Environment for Missile Interception Systems (ARTEMIS) which is under development for MDA for use in C2BMC.
Keywords: Debris Cloud, C2bmc, Bayesian Belief Network, Eo/Ir, Ballistic Missile Defense
