Spatio-Temporal Object Reconstructed Modeling System from Ground-Based Imagery of Space Objects

The Air Force is seeking innovative algorithms that automate and expedite the process of extracting four dimensional (4D) space-time imagery of space objects from ground-based space imagery collectors. In response to this need, Physical Optics Corporation (POC) proposes to develop a new Spatio-Temporal Object Reconstructed Modeling System (STORMS) based on a parallel genetic algorithm optimization engine (PGAOE), multiscale structure characterization (MSC), and spatial object dynamics (SOD) modeling. The concise framework of MSC for coarse-to-fine processing and the efficient and adaptive parallel genetic optimization algorithm engine for fast structural matching together enable STORMS to perform 4D modeling (3D spatial plus temporal) on space objects automatically, directly, and from an observed image sequences in less than 20 minutes. SOD modeling will coherently and accurately generate both the global motion dynamics and local dynamics of articulated space objects. The proposed STORM system not only meets the ARFL/DE needs, but has significant commercial applications in surveillance, homeland security, and autonomous robot navigation. In Phase I, POC will develop a prototype system for feasibility demonstration of 4D modeling from real image sequences of a space object. In Phase II, POC will optimize the STORMS to handle real data under less than ideal conditions.