2 September 1993 Recurrent neural networks for radar target identication
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A real-time recurrent learning algorithm was applied to a five class radar target identification problem. Wideband radar signatures were generated for five aircraft classes. Since an aircraft in flight is constantly in motion, a radar can measure sequences of radar signatures as the aspect angle changes. A radar can also generate aspect angle estimates by using kinematic information from aircraft position and velocity measurements. A recurrent neural network computer program (implementing a real time recurrent learning algorithm) was trained to recognize these sequences of radar signatures. Each radar signature was described by 6 external input features: the estimated target azimuth, the estimated target width, and 4 noisy amplitude values from 2 peak range bins. Nine consecutive radar signatures were sufficient to achieve a test set accuracy of 96%.
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Eric T. Kouba, Eric T. Kouba, Steven K. Rogers, Steven K. Rogers, Dennis W. Ruck, Dennis W. Ruck, Kenneth W. Bauer, Kenneth W. Bauer, } "Recurrent neural networks for radar target identication", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152541; https://doi.org/10.1117/12.152541

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