Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars. (7th March 2010)

• Record Type: Journal Article
• Authors: Vicen-Bueno Vicen-Bueno, R. R.; Carrasco-Álvarez Carrasco-Álvarez, R. R.; Rosa-Zurera Rosa-Zurera, M. M.; Nieto-Borge Nieto-Borge, J. C. J. C.; Jarabo-Amores Jarabo-Amores, M. P. M. P.
• Other Names: Ehlers Ehlers Frank Frank Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-03-07
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/380473
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs). In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical) integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus) integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.