Clutter Reduction Based on Principal Component Analysis Technique for Hidden Objects Detection

This paper brings a brief overview of the statistical method called Principal Component Analysis (PCA). It is used for clutter reduction in detection of hidden objects, targets hidden behind walls, buried landmines, etc. Since the measured data, imaged in time domain, suffer from the hyperbolic character of objects’ reflections, the utilization of the Synthetic Aperture Radar (SAR) method is briefly described. Besides, the basics of PCA as well as its calculation from the Singular Value Decomposition are presented. The principles of ground and clutter subtraction from image are then demonstrated using training data set and SAR processed measured data.

[1]  Fawzy Abujarad,et al.  Factor and Principle Component Analysis for Automatic Landmine Detection Based on Ground Penetrating Radar , 2005 .

[2]  Eric L. Miller,et al.  Model-based principal component techniques for detection of buried landmines in multiframe synthetic aperture radar images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[3]  B.G. Mobasseri,et al.  3D Classification of Through-the-Wall Radar Images Using Statistical Object Models , 2008, 2008 IEEE Southwest Symposium on Image Analysis and Interpretation.

[4]  M. J. Nigam,et al.  Analysis of Clutter Reduction Techniques for through Wall Imaging in UWB Range , 2009 .

[5]  Fawzy Abujarad,et al.  Ground penetrating radar signal processing for landmine detection , 2007 .

[6]  J. Scales Theory of Seismic Imaging , 1995 .

[7]  Glenn D. Boreman,et al.  Millimeter wave imaging system for the detection of nonmetallic objects , 2006, SPIE Defense + Commercial Sensing.

[8]  J. R. Lockwood,et al.  Alternatives for landmine detection , 2003 .

[9]  D. Niemeier,et al.  About the Authors , 2000 .

[10]  Kwang In Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Processing Letters.

[11]  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).

[12]  K. Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Process. Lett..

[13]  Jonathon Shlens,et al.  A Tutorial on Principal Component Analysis , 2014, ArXiv.

[14]  Mahmood R. Azimi-Sadjadi,et al.  Detection of mines and minelike targets using principal component and neural-network methods , 1998, IEEE Trans. Neural Networks.

[15]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[16]  D. Daniels Ground Penetrating Radar , 2005 .

[17]  Sevket Demirci,et al.  A synthetic aperture radar‐based focusing algorithm for B‐scan ground penetrating radar imagery , 2007 .