Performance comparison of total variation minimization and group sparse reconstructions for extended target imaging in multilayered dielectric media

Abstract

Imaging of targets embedded in multilayered dielectric media has attracted growing interest in microwave remote sensing, nondestructive testing, ground penetrating radar, and urban sensing. Compressive sensing has been successfully applied in the aforementioned applications for efficient target imaging, leading to prompt actionable intelligence. Recently, a total variation minimization (TVM) based approach was proposed, which offers superior performance over standard L1- minimization based sparse reconstruction in terms of target shape reconstruction and distinguishing closely-spaced point targets from an extended target. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for target extent. In this paper, we provide a performance comparison between TVM and GSR schemes for extended target imaging in multi-layered media using numerical electromagnetic data.