Vessel identification study for non-coherent high-resolution radar

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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