A Compressive-Sensing-Based Approach for the Detection and Characterization of Buried Objects

Abstract

The problem of determining and understanding the nature of buried objects by means of nondestructive and noninvasive techniques represents an interesting issue for a great variety of applications. In this framework, the theory of electromagnetic inverse scattering problems can help in such an issue by starting from the measures of the scattered field collected on a surface. What will be presented in this communication is a two-dimensional (2-D) technique based on the so-called Born approximation (BA) combined with a compressive sensing (CS) approach, in order to improve reconstruction capabilities for a proper class of targets. The use of a multiview-multistatic configuration will be employed together with a multifrequency approach to overcome the limited amount of data due to the single-frequency technique. Therefore, after a first numerical analysis of the performance of the considered algorithm, some numerical examples for 2-D aspect-limited configurations will be presented. The scenario is composed of a simplified scene, which consists of two half-spaces, and with the probes located close to the interface between the two media. As proposed in the following, it is easy to observe that the use of CS for this kind of problems may improve reconstruction capabilities, confirming the validity of the presented approach

Advanced Inversion Techniques for Ground Penetrating Radar

“…In such techniques, the inspected area is iteratively reconstructed at different scales and at each scale specific inversion methods are used to obtain a quantitative reconstruction of the dielectric properties. Compressive sensing techniques have also been successfully applied in the field of GPR imaging [46]- [48]. In such approaches, the imaging problem is recast as a minimization in L 1 functional spaces.…”

Compressive Sensing SFGPR Imaging Algorithm Based on Subspace Projection Ground Clutter Suppression

“…where ⊗ denotes Kronecker product; It is known that if Θ satisfies the restricted isometry property, the sparsity vector S can be recovered by solving the following problem min S l 1 subject toỸ = ΘS (15) A type of greedy algorithm called orthogonal matching pursuit (OMP) [23,24] is used to solve the problem to provide the fast and accurate result.…”

“…With the further development of CS theory, considerable work has been done to apply CS to SFGPR system establishment and image reconstruction in order to reduce the data acquisition time and improve the image quality [9][10][11][12][13][14][15][16]. In practical SFGPR measurement situation, since the distance between antennas and ground is very short, the wave reflected from the ground is much stronger than that from underground targets.…”

Inversion of Multifrequency Data With the Cross‐Correlated Contrast Source Inversion Method

“…The iterative algorithms have been further improved over the recent decades by using regularization constraint (Bauer et al, ; Sun et al, ; van den Berg et al, ), multiscaling technique (Caorsi et al, ), wavelet transformation (Li et al, ), etc. As an example, sparsity constraints can be appropriately exploited and compressive sensing‐based techniques are proved to be effective in both qualitative imaging (Gurbuz et al, ; Sun, Kooij, & Yarovoy, ; Sun, Kooij, Yarovoy, et al, ) and quantitative ones (Ambrosanio & Pascazio, ; Oliveri et al, ; Poli et al, ; Sun et al, ). However, it is worth noting that the cost functional remains unchanged in the aforementioned research work, which consists of two error terms: the data error and the state error.…”