2 October 2008 Neural network approaches for multi-spectral missile discrimination
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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.
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M. Can Altinigneli, M. Can Altinigneli, Sabino Gadaleta, Sabino Gadaleta, } "Neural network approaches for multi-spectral missile discrimination", Proc. SPIE 7113, Electro-Optical and Infrared Systems: Technology and Applications V, 711313 (2 October 2008); doi: 10.1117/12.799808; https://doi.org/10.1117/12.799808

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