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http://hdl.handle.net/10553/46148
| Título: | Vessel identification study for non-coherent high-resolution radar | Autores/as: | Carmona-Duarte, Cristina Ferrer-Ballester, Miguel Ángel Calvo-Gallego, Jaime Dorta-Naranjo, B. Pablo |
Clasificación UNESCO: | 3307 Tecnología electrónica | Palabras clave: | Support vector machines Radar imaging Doppler radar Frequency modulation Error analysis |
Fecha de publicación: | 2013 | Publicación seriada: | Proceedings - International Carnahan Conference on Security Technology | Conferencia: | 47th International Carnahan Conference on Security Technology (ICCST) 2013 47th International Carnahan Conference on Security Technology, ICCST 2013 |
Resumen: | This paper presents a vessel identification study based on vessel profile. The study was developed with real data obtained with high-resolution Continuous Wave Lineal Frequency Modulated (CW-LFM) radar. Cases studied in this work are vessels entering and leaving the harbor. Also, in this paper, a comparison between different classification techniques such as Neural Networks, Support Vector Machine and k-Nearest Neighbor is introduced. The differences between normalization methods are evaluated for each classification technique. | URI: | http://hdl.handle.net/10553/46148 | ISBN: | 9781479908899 | ISSN: | 1071-6572 | DOI: | 10.1109/CCST.2013.6922052 | Fuente: | Proceedings - International Carnahan Conference on Security Technology[ISSN 1071-6572] (6922052) |
| Colección: | Actas de congresos |
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