Real Time and Sparse Reconstructed Radar Imaging Through Stratified Media

The problem of imaging of objects within or through multilayered dielectric media appears in many areas, including those in ground-penetrating radar (GPR) imaging, through-the-wall radar imaging (TWRI), intra-wall and subsurface imaging, and medical imaging. In most practical situations the imaging of targets should be done in real-time, requiring the development of fast data acquisition schemes as well as highly efficient microwave imaging techniques that can fully account for wave propagation through various dielectric layers or walls.
In this lecture, an overview of various image reconstruction techniques for objects in stratified media will be given for both SAR-based and multiple-input multiple-output (MIMO) based systems, and for both real-time imaging and sparsity-based imaging scenarios. For the former, details of fast polarimetric and tomographic based imaging algorithms for 2D and 3D scenarios will be given, and imaging results for various realistic scenarios using both numerical simulations and laboratory experiments will be presented. Such fast-imaging techniques, however, do not address the problem posed by long data acquisition time associated with most microwave-imaging scenarios. To address this problem, one can resort to the use of Compressive Sensing (CS) to significantly reduce the number of antennas and/or collected frequency points. In our implementation of CS, the stratified media effects are accurately and efficiently accounted for in the sparse-image reconstruction. In particular, the use of total variation minimization (TVM) and its advantages over the l1-norm minimization, which is often used in the standard radar implementation of CS, will be detailed. Results for DT-based and TVM-based radar imaging in various GPR, subsurface inverse profiling, and TWRI scenarios will be given in the presentation.
Co-sponsored by: Univ of Michigan, Radlab
Speaker(s): Ahamd Hoorfar,
Room: 1500, Bldg: EECS Bldg, 1301 Beal Ave, Ann Arbor, Michigan, United States, 48109-2122