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The holographic subsurface radar (HSR) has been a promising geophysical electromagnetic technique for detecting shallowly buried targets with high lateral resolution image. However, radar images are considerably interpreted by strong reflections from rough surface and inhomogeneity in media of interest. In this article, we focus on mitigating the clutter in HSR applications using a learning-based approach, which requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference. The generative adversarial network (GAN) with attentive subspace projection is developed to remove the clutter and recover the target image. The subspaces containing target response are selected with the multihead attention preliminarily. Then, the generative network will further focus on the target regions, and the discriminative network will assess the generated results locally and globally. Experiments using real data were conducted to demonstrate the effectiveness of our approach. The visual and quantitative results show that the proposed approach achieves superior performance on removing clutter in HSR images compared with the state-of-the-art clutter mitigation approaches. [ABSTRACT FROM AUTHOR]

Holographic subsurface radar (HSR) is a promising geophysical electromagnetic technique to detect shallowly buried objects due to its high lateral resolution. However, the subsurface inspections and visualization of buried targets are prone to be impaired by strong clutter from the nonplanar surface reflections. In this article, a clutter mitigation method using dual-frequency cancellation and sparse feature enhancement is proposed for HSR data to distinguish the targets from background. The radar signals are received at two distinct frequencies under certain conditions to calculate the strong surface reflections. After cancellation of the estimated surface, a modified $\ell _{1}$ regularization is utilized to further mitigate the residual clutter and highlight the target signature. The effectiveness of the proposed method is evaluated on both numerical simulation and radar signals collected from real HSR systems. The visual and quantitative results demonstrate that the proposed method successfully removes the nonplanar surface clutter with the targets preserved. [ABSTRACT FROM AUTHOR]

Penetrating radar systems are widely employed to scan the objects that are placed behind or buried inside mediums (such as walls, ground, and so on). As the clutter is much stronger than the target echo, clutter removal must be performed before imaging. The moving average subtraction, spatial notch filtering, and singular value decomposition methods are commonly used to remove clutter. However, the drawback is that these methods eliminate some of the target spectrum information, which causes target energy losses and generates side lobes. To solve this problem, two spectrum recovery methods are proposed in this paper. The first method recovers the spectrum magnitude and phase via sinc interpolation and linear fitting, respectively, which is fast and suitable for real-time processing. Although the second method recovers the spectrum based on matrix completion with prior information, which is more accurate and more computational expensive. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed methods can improve various traditional clutter removal methods, the side lobes are clearly suppressed, and the signal-to-clutter ratio is significantly improved. [ABSTRACT FROM AUTHOR]