An automatic target cuer/recognizer for tactical fighters

This paper examines algorithms and techniques for use in a complete FLIR target cuer/recognizer. The application is the Air Force Theater Missile Defense Eagle Smart Sensor and Automatic Target Cuer/Recognizer (TESSA) program. The data used for this research are 1st generation FLIR images collected from an F-15E. The database contains thousands of images with various target arrangements. The specific target of interest is a mobile missile launcher, which will be defined as the primary target. The goal is to locate all vehicles (secondary targets) within a scene and identify the missile launchers. The system designed includes an image segmenter, region cluster algorithm, and classifier. Conventional algorithms in conjunction with neural network techniques are used to form a complete ATR system. Some of the conventional techniques include hit/miss filtering, difference of Gaussian filtering, and region clustering. A neural network (multilayer perceptron) is used for classification. These various algorithms are tested and combined into a functional ATR system. Overall target detection rate (cuer) is 84% with a 69% accurate primary target identification (recognizer) rate. Furthermore, the false alarm rate (a non-target cued as a target) is only 2.3 per scene. The research will be completed with a 10 flight test profile using an F-15E and will collect 3/sup rd/ generation FLIR images for use with these algorithms.