Clutter Reduction Based on Principal Component Analysis Technique for Hidden Objects Detection
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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, utilization of the Synthetic Aperture Radar (SAR) method is briefly described. Besides, the mathematical basics of PCA as well as its comparison with 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.
KeywordsSynthetic Aperture Radar, through-wall imaging, Singular Value Decomposition, Principal Component Analysis
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2012, vol. 21, č. 1, s. 464-470. ISSN 1210-2512
- 2012/1