Compact Overview of a Scheme for Sensing Shallowly-Buried Objects Beneath a Moderately Rough Interface
In ground penetrating radar (GPR) applications, a major source of pollution in the processed data is due to the distortion introduced by the twice-traversed rough air-soil interface, which the interrogating signal encounters on its way to and from the targets of interest. In some applications such as anti-personnel land mine remediation , where one typically has to deal with shallowly-buried small targets having constitutive properties very close to those of the background soil, this clutter may introduce severe constraints on target localization and classification. In this connection, fully-statistical approaches for clutter suppression, based on Monte-Carlo simulations, have been shown to work reasonably well in detection problems with small roughness  – , but have been found to perform rather poorly in applications involving target localization and classification in the presence of moderate roughness . These considerations motivated our investigations toward a more robust, physics-based, adaptive approach to subsurface sensing in the presence of a moderately rough air-soil interface (both in height and slope). The proposed approach, so far restricted to two-dimensional (2-D) geometries, relies on the use of physical and statistical modeling techniques to estimate, and compensate for, the related clutter. The algorithm is effective even with sparse data, and utilizes recently developed Gabor-based narrow-waisted Gaussian beam (GB) fast forward scattering solvers ,  (see also the review in ). Both frequency-stepped ,  and pulsed ,  GPR configurations have been investigated. In this paper, we give a compact review of the proposed framework and present representative results.
KeywordsGaussian Beam Ground Penetrate Radar Test Domain Ground Clutter Profile Reconstruction
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