MIMO Ground Penetrating Radar Imaging Through Multilayered Subsurface Using Total Variation Minimization

In most of the existing ground penetrating radar (GPR) imaging algorithms, using either full or sparse data collection, the ground is modeled as a single half-space layer. In this paper, a generalized sparse imaging approach with total variation minimization (TVM) for multiple-input multiple-output (MIMO) GPR imaging through multilayered subsurface is proposed. The multilayered media Green's function is incorporated in the imaging algorithm to take into account the complex wave propagation effects under multilayered subsurface. An analytical expression of the layered subsurface Green's function is derived using the stationary-phase method, which significantly reduces the computation time and complexity. On the other hand, as TVM minimizes the gradient of the image, its incorporation in the imaging algorithm results in a reconstruction that preserves the geometry and edges of the targets better than the standard L1-minimization-based sparsity-driven imaging. The number of antenna elements and frequency measurements in MIMO GPR system can be significantly reduced using the proposed technique without degradation of the image quality. Although MIMO configuration is investigated in this paper, the presented approach can be simply applied to monostatic synthetic aperture radar.