Vessel identification study for non-coherent high-resolution radar

Author

Carmona-Duarte, Cristina ; Ferrer-Ballester, Miguel Angel ; Calvo-Gallego, Jaime ; Dorta-Naranjo, B. Pablo

Author_Institution

Inst. Univ. para el Desarrollo Tecnol. y la Innovacion en Comun., Univ. of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

fYear

2013

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

CW radar; FM radar; neural nets; radar computing; radar signal processing; signal classification; support vector machines; CW-LFM radar; classification technique; high-resolution continuous wave lineal frequency modulated radar; k-nearest neighbor; neural networks; noncoherent high-resolution radar; normalization methods; support vector machine; vessel identification study; vessel profile; Doppler radar; Error analysis; Frequency modulation; Radar imaging; Support vector machines; Training; high-resolution radar; target identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Security Technology (ICCST), 2013 47th International Carnahan Conference on

Conference_Location

Medellin

Type

conf

DOI

10.1109/CCST.2013.6922052

Filename

6922052