<title>Neural network clutter-rejection model for FLIR ATR</title>

Abstract

Boundary shape is a significant and well-modeled property of forward-looking infrared (FUR) sensor target signatures. Stateof-the-art FLIR automatic target recognition (ATR) algorithms that rely on signature shape for target detection perform well but still fall short of human performance and many DoD requirements. We find that internal signature information significantly improves detection performance. The problem is that this information is not easily modeled, especially in FLIR signatures, because the signatures exhibit significant variations dependent on a large number of unknowns. We have developed a modeldriven neural network technique, called Programmed Constructive Neural Networks (PCNN), that demonstrates superior performance and generalization compared to traditional back-propagation techniques in high noise applications. We have used the PCNN technique to model internal FUR signature information for clutter rejection. Our PCNN FUR clutter rejection model eliminates 75% of the false alarms in a state-of-the-art shape-based algorithm with minimal detection loss. This result was achieved even on scenarios not represented in the training set.