Enhanced Detection of Hidden Targets Using Multi-Discriminant Ladar

The objective of this work is to develop ATR algorithms that use the spectropolarimetric information in multi-discriminant ladar images to segment, detect and identify hidden targets. Hidden targets using camouflage, concealment and deception are difficult to detect with standard weapons sensors including basic ladar systems. A multi-discriminant ladar having polarized, multi-spectral, and multi-pulse features is expected to provide a significant enhancement to the target detection process, but ATR algorithms must be developed to take advantage of these multi-discriminant attributes. The Phase I effort, which used synthetic ladar images, has demonstrated several promising algorithms based on novel formulations of spectropolarimetric feature vectors used with manifold learning/clustering techniques; however, these algorithms must be refined, extended and verified by application to measured multi-discriminant ladar images, which is the goal of the Phase II program. The use of multi-discriminant LEAP ATR algorithms tailored to AFRL experimental ladar systems will facilitate the rapid assessment of the compatibility of the ATR algorithms contemporaneously with the laboratory and field tests.