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This article presents detailed geometric analyses of ground clutter in bistatic passive airborne radars. Analytic closed-form solutions are derived for finding the intersection of iso-ranges and iso-velocities. This allows clutter bistatic coordinates to be easily converted to its Cartesian coordinates. Based on those solutions, a theoretical clutter map in the bistatic coordinates can be calculated assuming uniform clutter distribution on the Earth’s surface. This is done by first converting a resolution cell in the bistatic coordinates into a corresponding cell in the Cartesian coordinates. Next, the power distribution in the bistatic coordinates is calculated taking into account range dependency, incident angles, and the radiation patterns of the transmitter and the receiver. The aim of this analysis is to characterize clutter in terms of expected mean power map rather than in terms of statistical distribution. The obtained results provide insights into the expected characteristics of clutter, which can be helpful in designing signal processing algorithms for ground moving target indication (GMTI) and synthetic aperture radar (SAR). The theoretical clutter distribution on the range-velocity map is compared with real-life data acquired with a Digital Video Broadcasting–Terrestrial (DVB-T)-based passive radar and good agreement between theory and measurement is presented. [ABSTRACT FROM AUTHOR]

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]