Efficient Clutter Suppression and Nonlinear Filtering Techniques for Tracking Dim Closely Spaced Objects in the Presence of Debris

EO/IR elements of the Ballistic Missile Defense System (BMDS) responsible for detecting and tracking ballistic missile threats encounter extraordinarily challenging threat and scene phenomenology. Specifically, non-stationary clutter characteristic of airborne and satellite-based sensor systems, along with dim target signatures, closely-spaced objects, and dense debris clouds typical of ballistic threats in midcourse flight, present complications for accurately detecting, tracking, and engaging ballistic threats across the BMDS. Current Detect-then-Track algorithms are extremely vulnerable to high false alarm rates under these circumstances. At a system level, the problem is much more catastrophic; detections from multiple sensors overwhelm the system making multisensor integration difficult. Due to range deficiencies of EO/IR sensor technology, multisensor integration is vital for successful intercept of ballistic threats. To address these challenges, we propose a framework that leverages spatiotemporal image processing algorithms for non-stationary clutter estimation and rejection, and nonlinear filtering based Track-before-Detect algorithms for tracking dim targets. Our approach fuses information across sensors without loss of information due to detection thresholds. Our algorithms, when applied jointly, provide a near-optimal solution. In addition, our algorithms are capable of resolving dim closely-spaced objects and robustly handle nonlinearities from: threat trajectories, closely-spaced object/debris phenomenology, and 3D-to-2D projective nonlinearities typical of optical sensors.