Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

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.

[1]  R Vicen-Bueno,et al.  Sea clutter power reduction in radar measurement systems by feedforward multilayer perceptrons with medium input data integration rate , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[2]  Inder J. Gupta,et al.  A novel signal processing technique for clutter reduction in GPR measurements of small, shallow land mines , 2000, IEEE Trans. Geosci. Remote. Sens..

[3]  Henry Leung,et al.  Signal detection using the radial basis function coupled map lattice , 2000, IEEE Trans. Neural Networks Learn. Syst..

[4]  Dragana Carevic.  Clutter reduction and target detection in ground-penetrating radar data using wavelets , 1999, Defense, Security, and Sensing.

[5]  Katrin Hessner,et al.  Signal-to-noise ratio analysis to estimate ocean wave heights from X-band marine radar image time series , 2008 .

[6]  Vahid Tabataba Vakili,et al.  Introducing Switching Ordered Statistic CFAR Type I in Different Radar Environments , 2009, EURASIP J. Adv. Signal Process..

[7]  A. Harati-Mokhtari,et al.  Automatic Identification System (AIS): Data Reliability and Human Error Implications , 2007, Journal of Navigation.

[8]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[9]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[10]  P. Jarabo-Amores,et al.  Moving ship detection in presence of sea clutter from temporal sequences of marine radar images , 2008, 2008 International Conference on Radar.

[11]  Jens Dannenberg,et al.  X-Band Radar as a Tool to Determine Spectral and Single Wave Properties , 2006 .

[12]  Simon Watts.  The performance of cell-averaging CFAR systems in sea clutter , 2000, Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037].

[13]  R. Stouffer,et al.  World Meteorological Organization , 1954, International Organization.

[14]  Dragana Carevic.  Clutter Reduction and Detection of Minelike Objects in Ground Penetrating Radar Data Using Wavelets , 2000 .

[15]  Alan J. Dix,et al.  Enabling Automatic Clutter Reduction in Parallel Coordinate Plots , 2006, IEEE Transactions on Visualization and Computer Graphics.

[16]  Lawrence F. Gray,et al.  A Mathematician Looks at Wolfram''s New Kind of Science , 2003 .

[17]  Francisco López-Ferreras,et al.  Study of Two Error Functions to Approximate the Neyman–Pearson Detector Using Supervised Learning Machines , 2009, IEEE Transactions on Signal Processing.

[18]  Jan Larsen,et al.  Independent component analysis for clutter reduction in ground penetrating radar data , 2002, SPIE Defense + Commercial Sensing.

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[21]  Henry Leung,et al.  Sea-clutter modeling using a radial-basis-function neural network , 2001 .

[22]  Simon Haykin,et al.  Neural networks , 1994 .

[23]  Friedwart Ziemer,et al.  RADAR SURVEY OF NEAR SHORE BATHYMETRY WITHIN THE OROMA PROJECT , 2004 .

[24]  R. S. Raghavan.  Analysis of CA-CFAR processors for linear-law detection , 1992 .

[25]  Hakan O. Brunzell.  Clutter reduction and object detection in surface penetrating radar , 1997 .

[26]  W. Marsden.  I and J , 2012 .

[27]  Alan J. Dix,et al.  A Taxonomy of Clutter Reduction for Information Visualisation , 2007, IEEE Transactions on Visualization and Computer Graphics.

[28]  H.B.D. Sorensen,et al.  Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[29]  Zoubeida Messali,et al.  Performance of Distributed CFAR Processors in Pearson Distributed Clutter , 2007, EURASIP J. Adv. Signal Process..

[30]  D. de la Mata-Moya,et al.  Analysis of the sea clutter structure using temporal sequences of X-band marine radar images , 2008, 2008 International Conference on Radar.

[31]  Manuel Rosa-Zurera,et al.  Sea Clutter Reduction and Target Enhancement by Neural Networks in a Marine Radar System , 2009, Sensors.

[32]  Saleem A. Kassam,et al.  Analysis of CFAR processors in homogeneous background , 1988 .