A Clutter Suppression Algorithm for GPR Data Based on PCA Combining with Gradient Magnitude

Abstract:

Ground penetrating radar (GPR) is a powerful tool for detecting defects behind reinforced concrete (RC) structures. However, the received data from GPR includes a large number of clutters which are easy overwhelming the signal of target. In order to successfully extract the target signature, these clutters effects need to be eliminated. In this article, a clutter suppression algorithm based on Principal Component Analysis (PCA) combining with gradient magnitude is presented. PCA clutter suppression algorithm is applied to the data and removes most of the echoes from ground surface and portion of other clutters with weak energy. Then gradient magnitude clutter suppression is used to remove majority of the residue clutters. It is demonstrated from simulation that the proposed algorithm is able to significantly suppress the clutters and is superior to the PCA clutter suppression, magnitude clutter suppression and means subtraction method.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 1662-1667
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.1662
• Online since: September 2014
• Authors: Qing Qing Lu*, Jie Xin Pu, Xiao Hong Wang, Zhong Hua Liu
• Keywords: Clutter Suppression, GPR, Gradient Magnitude, PCA

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