Adaptive Ground Clutter Reduction in Ground-Penetrating Radar Data Based on Principal Component Analysis

Abstract

Singular value decomposition is an effective way to remove ground clutter in ground-penetrating radar (GPR) applications. The main limitation of this method is the selection of principal components to completely reconstruct the ground clutter or the target. To date, no effective criteria or technology have been developed. To solve this problem, a new method is proposed in this paper. The research and analysis presented herein reveal that the root-mean-square height (RMSH) of the first-arrival curve corresponding to the ground clutter has a well-defined positive relationship with the number of singular values associated with the principal components of the ground clutter. The number of singular values of these principal components ( $N$ ) can be precisely determined based on the ground clutter by a linear function, $N = 0.2634D + 1.3086$ , where D represents the RMSH value. In addition, an algorithm called developed histogram equalization was developed to improve the contrast to highlight the targets in denoized GPR data sets. The proposed strategy of extracting the principal components of the ground clutter and highlighting the contrast between the target signal and environmental reflections was successfully applied to the field GPR data, thus demonstrating the practicality and validity of the proposed approach.