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.

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.

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.

In GPR prospecting, strong reflections due to the background material interface can hinder the detection of localized buried scatterers, especially when the targets are close (in terms of probing wavelength) to the interface. Moreover, signals due to objects located outside the investigated domain and occurring in the observation time window may dramatically affect the reliability of the results. In order to mitigate such kind of clutter, an entropy based approach has been recently proposed in the frame of intra-wall diagnostic. In this paper, we assess the performance of such an approach by processing experimental data collected in laboratory controlled conditions and referred for the challenging situation of shallower dielectric and metallic targets, whose back-scattered fields overlap in time with the air-soil interface signal. In addition, a performance comparison of the proposed method is performed with other two approaches, i.e., the mean subtraction method and the subspace projection procedure.

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.

Microwave imaging is an emerging breast cancer diagnostic technique, which aims at complementing already established methods like mammography, magnetic resonance imaging, and ultrasound. It offers two striking advantages: no-risk for the patient and potential low-cost for national health systems. So far, however, the prototypes developed for validation in labs and clinics used costly lab instruments such as a vector network analyzer (VNA). Moreover, the CPU time required by complex image reconstruction algorithms may not be compatible with the duration of a medical examination. In this paper, both these issues are tackled. Indeed, we present a prototype system based on low-cost and off-the-shelf microwave components, custom-made antennas, and a small form-factor processing system with an embedded field-programmable gate array for accelerating the execution of the imaging algorithm. We show that our low-cost system can compete with an expensive VNA in terms of accuracy, and it is more than 20x faster than a high-performance server at image reconstruction.