Abstract
Sparse reconstruction methods have been successfully applied for efficient radar imaging of targets embedded in stratified dielectric subsurface media. Recently, a total variation minimization (TVM) based approach was shown to provide superior image reconstruction performance over standard L1-norm minimization-based method, especially in case of non-point-like targets. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for embedded target extent. In this paper, we provide qualitative and quantitative performance evaluations of TVM and GSR schemes for efficient and reliable target imaging in stratified subsurface media. Using numerical electromagnetic data of targets buried in the ground, we demonstrate that GSR and TVM provide comparable reconstruction performance qualitatively, with GSR exhibiting a slight superiority over TVM quantitatively, albeit at the expense of less flexibility in regularization parameters.
Highlights
Effective and reliable imaging of targets embedded in stratified subsurface media is highly desirable in ground penetrating radar (GPR) applications [1,2,3,4,5,6,7,8,9,10,11,12,13]
We provide a performance evaluation of total variation minimization (TVM) and group sparse reconstruction (GSR) schemes for non-point-like target output (MIMO) radar system and use numerical electromagnetic data of targets buried in a four‐
We show that the two methods provide comparable performance qualitatively under image reconstruction, especially at low signal‐to‐noise ratio (SNR) values
Summary
Effective and reliable imaging of targets embedded in stratified subsurface media is highly desirable in ground penetrating radar (GPR) applications [1,2,3,4,5,6,7,8,9,10,11,12,13]. The sparse nature of targets has been successfully exploited in GPR image recovery through sparse reconstruction approaches [4,17,18,19,20,21]. Most sparsity-based methods model the background environment in GPR as a two-layered medium, where radar operates in the upper air layer and the targets are buried within the lower ground layer. A generalized sparse image reconstruction approach with total variation minimization (TVM) was proposed for efficient and reliable radar imaging through multilayered background media [4]
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