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.