Raffaele Solimene

Through-Wall Imaging via a Linear Inverse Scattering Algorithm

A through-wall imaging problem for a 2-D scalar geometry is addressed. It is cast as an inverse scattering problem and tackled under the linear model of the electromagnetic scattering that is provided by the Born approximation. A truncated singular value decomposition inversion scheme is exploited, and the performances that are achievable by such an inversion scheme are assessed by exploiting synthetic data. The cases of weakly and strongly scattering objects are both considered. Finally, an example of reconstruction that is obtained by exploiting experimental data is presented.

Three-Dimensional Through-Wall Imaging Under Ambiguous Wall Parameters

A through-wall imaging problem for a 3-D geometry is considered. Scatterers are located beyond a wall represented by a dielectric slab whose features are unknown or known with some degree of uncertainty. A two-step imaging procedure is presented. First, the thickness and the dielectric permittivity of the wall are estimated by a simple procedure which takes into account that actual measurements concern the total scattered field (i.e., the field reflected by the wall plus the one scattered by the obscured scatterers). Then, the problem is cast as a linear inverse scattering problem and solved by means of a truncated-singular value decomposition algorithm. In particular, a 2-D sliced approach is employed to obtain the 3-D scene. Numerical examples are shown to assess the effectiveness of the reconstruction procedure.

A Multiarray Tomographic Approach for Through-Wall Imaging

A through-wall imaging problem for a 2-D scalar geometry is addressed. It is cast as an inverse scattering problem and tackled under the linear Born model by means of the truncated singular value decomposition inversion scheme. A multiarray-based inversion strategy is considered. In particular, first the data collected by each single array are processed to obtain different tomographic images of the same scene under test. Then, the different images are suitably combined to obtain the overall image. The inversion scheme is tested for the challenging case of objects located within a complex environment resembling a room in a building.

Sparse Reconstruction From GPR Data With Applications to Rebar Detection

The problem of detecting and localizing 2-D thin scatterers (i.e., elongated scatterers whose cross sections are small in terms of the probing wavelength) from scattered field measurements is considered. To this end, a linear model that neglects mutual scattering and is based on a distributional representation of the unknown is established. An improved imaging technique based on a minimization algorithm, which takes advantage of the inherent sparseness of the considered ground-penetrating radar scenario, is presented and compared to a classical migration algorithm. The comparison is achieved for both synthetically generated and experimental data collected in realistic conditions under a multimonostatic/multifrequency configuration.

A Simple Strategy to Detect Changes in through the Wall Imaging

In this paper, a simple strategy to detect changes in through-the-wall imaging scenarios is presented. In particular, tomographic reconstructions taken at different instants of time are exploited. This allows to increase the detectability of scatterers whose positions are varied in two different data collections. The feasibility of the technique is demonstrated with both synthetic and experimental data.

Localization of the Interfaces of a Slab Hidden Behind a Wall

In this paper, a 1-D inverse-scattering problem laying within the framework of through-wall imaging is addressed. In particular, the problem of localizing the interfaces of a slab hidden behind an obstacle, another slab whose electromagnetic features and thickness are known, is considered. To this end, an approximate linear mathematical relationship between the scattered field and the unknown slab-interface positions is stated. Such an approximate relationship arises from neglecting the multiple-reflections between the two unknown slabs interfaces and between the slab and the obstacle. The unknown locations of the slabs interfaces are represented as the support of Dirac-delta functions, and the problem is cast as the inversion of a linear integral operator whose inversion is achieved by means of the Truncated-Singular-Value-Decomposition (TSVD) inversion scheme. The effect of the parameters of the obstacle on the inversion algorithm and the performances achievable by the solution approach are assessed by exploiting synthetic data. Furthermore, a comparison with the reconstructions obtained under the Born approximation and with the time-backscattered field is achieved. Finally, results obtained by employing experimental data collected owing to a stepped-frequency ground-penetrating radar system are also presented.

SAR Imaging Algorithms and Some Unconventional Applications: A unified mathematical overview

This article deals with two significant aspects related to synthetic aperture radar imaging (SAR-I) of relevant theoretical and applicative interest. The first objective regards the analysis of the most-used SAR-I approaches under the unified mathematical framework provided by the Porter-Bojarski integral equation. The second objective is to provide an updated overview on how SAR-I research is generalizing previous algorithms to deal with unconventional scenarios.

Three-Dimensional Microwave Tomography by a 2-D Slice-Based Reconstruction Algorithm

This letter deals with the problem of imaging 3-D strong scatterers by means of a 2-D slice tomographic reconstruction algorithm. More in detail, the inversion algorithm is based on two steps. In the first step, 2-D slices of the object are reconstructed; then, they are superimposed and interpolated to achieve the 3-D representation of the object. The Kirchhoff approximation is exploited to develop a linear inversion scheme for reconstructing the 2-D slices for scatterers embedded in the free space. Finally, the inversion algorithm is checked against synthetic data for the interesting case of scatterers resembling the human body.

Performance Analysis of Time-Reversal MUSIC

In this paper, we study the performance of multiple signal classification (MUSIC) in computational time-reversal (TR) applications. The analysis builds upon classical results on first-order perturbation of singular value decomposition. The closed form of mean-squared error (MSE) matrix of TR-MUSIC is derived for the single-frequency case in both multistatic co-located and non co-located scenarios. The proposed analysis is compared with Cramér-Rao lower-bound (CRLB), and it is exploited for comparison of TR-MUSIC when linear and (nonlinear) multiple-scattering is present. Finally, a numerical analysis is provided to confirm the theoretical findings.

TWI for an Unknown Symmetric Lossless Wall

A through-wall-imaging problem where the scatterers are located beyond a 1-D nonhomogeneous unknown wall is considered. As is well known, as part of the propagation takes place within the wall, the knowledge of its electromagnetic properties is mandatory in order to obtain properly focused images. Accordingly, this papers focus is on the wall estimation problem. To this end, a simple procedure to estimate the wall transmission coefficient is introduced and validated by synthetic data. The proposed estimation procedure works for a symmetric lossless wall and does not rely on optimization schemes but requires a multistatic configuration. As a proof of the principle, we address the problem for a 2-D scalar configuration. Moreover, the role of obscured scatterers on the estimation procedure is discussed, and a procedure for mitigating their effect is proposed as well. Finally, the procedure is also checked against a wall with losses.