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Progress In Electromagnetics Research C
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CLASS IDENTIFICATION OF AIRCRAFTS BY MEANS OF ARTIFICIAL NEURAL NETWORKS TRAINED WITH SIMULATED RADAR SIGNATURES.

By A. Jurado-Lucena, I. Montiel-Sanchez, D. Escot-Bocanegra, R. Fernandez-Recio, and D. Poyatos-Martınez

Full Article PDF (787 KB)

Abstract:
Non-Cooperative Target Recognition (NCTR) of aircrafts from radar measurements is a formidable problem that has drawn the attention of engineers and scientists over the last years. NCTR techniques typically involve a database with a huge amount of information from different known targets and a reliable identification algorithm able to highlight the likeness between measured and stored data. This paper uses High Resolution Range Profiles produced with a high-frequency software tool to train Arti cial Neural Networks for distinguishing between different classes of aircrafts. Actual data from the ORFEO measurement campaign are used to assess the performance of the trained networks.

Citation:
A. Jurado-Lucena, I. Montiel-Sanchez, D. Escot-Bocanegra, R. Fernandez-Recio, and D. Poyatos-Martınez, "Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures.," Progress In Electromagnetics Research C, Vol. 21, 243-255, 2011.
doi:10.2528/PIERC11030206

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