Overview

Multi-color infrared imaging missile-warning systems require real-time detection techniques that can process the wide instantaneous field of regard of focal plane array sensors with a low false alarm rate. Current technology applies classical statistical methods to this problem and ignores neural network techniques. Thus the research reported here is novel in that it investigates the use of radial basis function (RBF) neural networks to detect sub-pixel missile signatures. An RBF neural network is designed and trained to detect targets in two-color infrared imagery using a recently developed regression tree algorithm. Features are calculated for 3 by 3 pixel sub-images in each color band and concatenated into a vector as input to the network. This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

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