Recurrent neural networks for radar target identication

Eric T. Kouba; Steven K. Rogers; Dennis W. Ruck; Kenneth W. Bauer Jr.

doi:10.1117/12.152541

2 September 1993 Recurrent neural networks for radar target identication

Eric T. Kouba, Steven K. Rogers, Dennis W. Ruck, Kenneth W. Bauer Jr.

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152541
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

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%.

Citation Download Citation

Eric T. Kouba, Steven K. Rogers, Dennis W. Ruck, and Kenneth W. Bauer Jr. "Recurrent neural networks for radar target identication", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152541

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