Artificial neural network-based clutter reduction systems for ship size estimation in maritime radars

Authors:
R. Vicen-Bueno;R. Carrasco-Álvarez;M. Rosa-Zurera;J. C. Nieto-Borge;M. P. Jarabo-Amores
Affiliations:
Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Madrid, Spain;Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Madrid, Spain;Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Madrid, Spain;Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Madrid, Spain;Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Madrid, Spain
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
Year:
2010

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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.