Adaptive spatial-temporal filtering methods for clutter removal and target tracking

In space-based infrared (IR) ballistic missile defense sensor systems, cluttered backgrounds are typically much more intense than the equivalent sensor noise or the targets being detected. Therefore, the development of efficient clutter removal and target preservation/enhancement algorithms is of crucial importance. To meet customer requirements, the advanced clutter rejection algorithms should provide more than 20 dB improvement in detection sensitivity. We propose an adaptive parametric spatial-temporal filtering technique together with the jitter compensation (scene stabilization). The results of simulations and processing of real data show that the developed adaptive spatial-temporal clutter suppression (CLS) algorithms allow for efficient clutter rejection in all tested situations. Proposed algorithms completely remove heavy clutter in the presence of substantial jitter and do not require expensive subpixel jitter stabilizers. In contrast, spatial-only filters and temporal differencing methods can be used only for weak and relatively correlated clutter. A stand-alone simulator was developed to demonstrate capabilities and performance of various algorithmic approaches. Simulations model various geometries, resolutions, illuminations, and meteorological conditions for space-based IR staring sensor systems.