Robust Algorithms Enhance Target Detection and Aim Point Selection

An architecture that eliminates false targets, null signals, and noise from IR missile seeker data is proposed. Smart algorithms mask false targets and data of no interest from the seeker image processing pipeline. The approach leverages algorithms developed for medicine and military programs whose performance was confirmed in the Phase I on representative missile data. The technology dramatically lowers false alarm rates and lowers cost while buying back bandwidth and processing time for target detection, target discrimination, and aimpoint selection improvements. In Phase II, the algorithm will be developed further and refined using comprehensive data sets provided by the THAAD prime contractor. The algorithm will then be implemented in hardware emulating next generation seekers and demonstrated. A hardware testbed will be used to assess performance, demonstrate the advantages of the algorithm, and serve as the starting point for integration into current and planned weapons systems. System improvements will be measured in terms of a quantifiable increase in probability of detection rates, lowered false alarm rates, and reduction of pixels in the image processing pipeline in scenarios of low signal and high noise. Polaris Sensor Technologies is collaborating with commercial sensor manufacturers to port this technology into commercial systems.