Infrared target-flare discrimination using a ZISC hardware neural network

Abstract

The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile’s field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.