Enhanced Algorithms for EO/IR Electronic Stabilization, Clutter Suppression, and Track-Before-Detect for Multiple Low Observable Targets

We develop and evaluate a suite of advanced algorithms which provide significantly-improved capabilities for finding, fixing, and tracking multiple ballistic and flying low observable objects in highly stressing cluttered environments. The algorithms have been developed for use in satellite-based staring and scanning optical surveillance suites for applications including theatre and intercontinental ballistic missile early warning, trajectory prediction, and multi-sensor track handoff for midcourse discrimination and intercept. The system performs sensor motion compensation providing sub-pixel stabilization (to 1/100 of a pixel), as well as advanced spatial-temporal clutter estimation and suppression to below sensor noise levels, followed by statistical background modeling and nonlinear Bayesian multiple-target trackbefore-detect filtering. The multiple-target tracking is performed in physical world coordinates to allow for multisensor fusion, trajectory prediction, and intercept. The algorithms are designed to handle a wide variety of real-world challenges: the scene background may contain significant celestial, earth limb, or terrestrial clutter, and the targets of interest may be dim, relative to the scene background. The performance of the developed algorithms is demonstrated using real-world data containing resident space objects observed from the MSX platform, with backgrounds varying from celestial to combined celestial and earth limb. Simulation results are also presented for parameterized variations in signal-to-clutter levels (down to 1/1000) and signal-to-noise levels (down to 1/6) for simulated targets against real-world terrestrial clutter backgrounds.