RSTA Research of the Colorado State, University of Massachusetts and Alliant Techsystems Team

The complementary nature of LADAR, FLIR and color data for ATR is being exploited by new algorithms in a three stage recognition system. The stages are initial detection, target class and pose hypothesis generation, and precise model to multisensor coregistra-tion matching. Coregistration globally aligns 3D target models with range, IR and color imagery while simultaneously reening registration parameters between sensors. This model directed approach is expected to improve ATR performance for occluded targets, targets seen at unusual angles, and targets in cluttered settings. Color is used for initial target detection under daylight conditions and camouuage learned from training generalizes across vehicles and distinguishes targets from natural terrain. Target class and pose hypothesis generation will draw upon existing LADAR boundary matching work extended to tolerate more oc-clusion, clutter and viewpoint variation. New model to multisensor coregistration algorithms appear robust in early tests and are the basis for future coregistration matching. A new interactive 3D visualization environment allows inspection of multisensor data, coregistration, and monitoring of recognition.