3D imaging for ground-penetrating radars via dictionary dimension reduction

Abstract

Ground-penetrating radar (GPR) has been widely used in detecting or imaging subsurface targets. In many applications such as archaeology, utility imaging, or landmine detection, three-dimensional (3D) images of the subsurface region is required for better understanding of the sensed medium. However, a high-resolution 3D image requires wideband data collection both in spatial and time/frequency domains. Match filtering is the main tool for generating subsurface images. Applying match filtering with the data acquisition impulse response for each possible voxel in the 3D region with the collected data requires both a tremendous amount of computer memory and computational complexity. Hence, it is very costly to obtain 3D GPR images in most of the applications although 3D images are very demanded results. In this paper, a new 3D imaging technique is proposed that will first decrease the memory requirements for 3D imaging with possible implications for less computational complexity. The proposed method uses the shifted impulse response of the targets that are on the same depth as a function of scanning position. This similarity of target responses for data dictionaries for only 2D target slices is constructed with twice the length in scanning directions and this 2D dictionary is mainly used for generating 3D images. The proposed method directly saves memory due to dimension reduction in dictionary generation and also decreases computational load. Simulation results show generated 3D images with the proposed technique. Comparisons in both memory and computational load with the standard backprojection show that the proposed technique offers advantages in both areas.