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Ultra-wideband (UWB) ground-penetrating radar (GPR) technology has been widely employed for detecting targets that range from buried explosive devices such as landmines, improvised explosive devices (IEDs), to underground utilities and tunnels, etc. However, the backscatter signals from the ground surface pose a critical challenge for downward-looking GPR systems since (i) these ground return signals have significant power compared to the backscatter signal from subsurface targets, and (ii) the ground return and target signals completely overlap in both the time and frequency domains. Many techniques have been proposed to date; however, they all have limitations in mitigating the adverse effects of the very high power ground return interference (GRI) signals. This paper presents a novel technique for reconstructing and extracting the GRI signals from downward-looking UWB GPR signals. Our proposed technique performs an estimation of the return signal from the ground surface. This signal estimation, together with the estimated scatter center of the ground surface, is used to construct a dictionary that represents the ground return signal subspace. Finally, we employ a sparsity-driven optimization algorithm to reconstruct the GRI signals and then extract them from the received radar signals. All information used to construct the dictionary is completely derived from the data. Our technique performs this GRI extraction directly in the phase history data domain prior to synthetic aperture radar (SAR) image formation. Thus, it can be implemented as an additional step, completely independent from all other steps, in the pre-processing stage. Recovery results from simulated data set illustrate the robustness and effectiveness of our proposed technique.
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