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Proceedings Paper

Neural network approaches for multi-spectral missile discrimination
Author(s): M. Can Altinigneli; Sabino Gadaleta
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Paper Abstract

Missile Warning Systems (MWS) have the task to identify missile threats to support timely counter measures. Key difficulty is that anything which can be detected must be considered a potential alarm at the output of the classifier. Hence, MWS must be optimized at a certain threshold on the receiver operating characteristic to trade probability of declaration against false alarm rate. To identify actual threats, two neural-network based discrimination algorithms are presented. In the first approach, measured object features from each spectral band over time are used to derive temporal features that model the temporal object behavior. These temporal features are fed into a static neural network. In the second approach, the measured object features are fed directly into a dynamic neural network which has a context layer. We present performance results of the two approaches based on simulated missile data overlaid with recorded background data.

Paper Details

Date Published: 2 October 2008
PDF: 10 pages
Proc. SPIE 7113, Electro-Optical and Infrared Systems: Technology and Applications V, 711313 (2 October 2008); doi: 10.1117/12.799808
Show Author Affiliations
M. Can Altinigneli, EADS Deutschland GmbH (Germany)
Sabino Gadaleta, EADS Deutschland GmbH (Germany)

Published in SPIE Proceedings Vol. 7113:
Electro-Optical and Infrared Systems: Technology and Applications V
David A. Huckridge; Reinhard R. Ebert, Editor(s)

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