Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2010, Article ID 380473, 15 pages
Artificial Neural Network-Based Clutter Reduction Systems for
Ship Size Estimation inMaritime Radars
R. Vicen-Bueno, R. Carrasco-Álvarez, M. Rosa-Zurera (EURASIPMember),
J. C. Nieto-Borge, andM. P. Jarabo-Amores
Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Alcalá de Henares,
28805 Madrid, Spain
Correspondence should be addressed to R. Vicen-Bueno, email@example.com
Received 1 July 2009; Revised 16 November 2009; Accepted 14 January 2010
Academic Editor: Frank Ehlers
Copyright © 2010 R. Vicen-Bueno et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
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.
The measurement of physical parameters of the sea surface by