L. Di Donato

Microwave Imaging of Nonweak Targets via Compressive Sensing and Virtual Experiments

Compressive sensing (CS)-based techniques can represent a very attractive approach to inverse scattering problems. In fact, if the unknown has a sparse representation and the measurements are properly organized, CS allows to considerably reduce the number of measurements and offers the possibility to achieve optimal (or nearly optimal) reconstruction performance. Unfortunately, the inverse scattering problem is nonlinear, while CS theory is well established only for linear recovery problems. As a contribution to overcome this issue, in this letter, we introduce two different CS-inspired approaches that exploit the “virtual experiments” framework, wherein it is possible to cast the inverse scattering problems in a linear form even in the case of nonweak targets.

Quasi — Invisibility via inverse scattering techniques

The paper investigates the feasibility of an approach to design effective cloaking devices by means of a joint exploitation of available degree of freedom in synthesis strategies and the physical mechanism underlying cloaking. A different point of view in the design of dielectric covers that minimize the scattering cross section of dielectric objects is introduced and new opportunities are outlined to perform an effective synthesis of cloaking profiles via inverse scattering techniques.

A New Strategy to Constrained Focusing in Unknown Scenarios

We introduce a novel strategy to synthesize an antenna array capable to focus a field in an unknown scenario. The underlying power synthesis problem is efficiently solved via convex programming and using an approximation of the fields radiated by the array elements. This approximation stems from an original exploitation of the equation underlying the Linear Sampling Method. As such, the proposed strategy takes advantage of the broad applicability of this well-known imaging approach. Some numerical examples are given to illustrate the performance of the strategy.

Improved TV-CS Approaches for Inverse Scattering Problem.

Total Variation and Compressive Sensing (TV-CS) techniques represent a very attractive approach to inverse scattering problems. In fact, if the unknown is piecewise constant and so has a sparse gradient, TV-CS approaches allow us to achieve optimal reconstructions, reducing considerably the number of measurements and enforcing the sparsity on the gradient of the sought unknowns. In this paper, we introduce two different techniques based on TV-CS that exploit in a different manner the concept of gradient in order to improve the solution of the inverse scattering problems obtained by TV-CS approach. Numerical examples are addressed to show the effectiveness of the method.

An over-the-distance wireless battery charger based on RF energy harvesting

An RF powered receiver silicon IC (integrated circuit) for RF energy harvesting is presented as wireless battery charger. This includes an RF-to-DC energy converter specifically designed with a sensitivity of −18.8 dBm and an energy conversion efficiency of ∼45% at 900 MHz with a transmitting power of 0.5 W in free space. Experimental results concerned with remotely battery charging using a complete prototype working in realistic scenarios will be shown.

Inverse scattering and compressive sensing as advanced e.m. design tools

In this paper we propose the adoption of the inverse scattering theory for the synthesis of profiles which are able to scatter an imposed scattered field. The design procedure moves from the Contrast Source Inversion (CSI) method. Moreover, by taking inspiration from the ℓ1-norm penalized least square problem, a compressive sensing (CS) inspired inversion scheme is considered for the synthesis of sparse dielectric profiles.

Electromagnetic waves spatial focusing: Issues, applications and comparisons

Spatially focusing a wavefield (or better the corresponding intensity) into a target point is a canonical problem relevant both theoretically and practically. In this communication, after briefly reviewing some critical aspects of such a problem, we provide an update of some focusing approaches we have recently developed. Numerical results and comparisons are also reported.

New tomographic imaging strategies for GPR surveys

Tomographic methods nowadays represent an assessed means to process GPR data, as they allow to obtain images that are more reliable and readable than those achieved using standard GPR data processing tools. However, tomographic approaches are based on a suitable modeling of the electromagnetic scattering phenomenon, which is required to cast the underlying inverse problem. In particular, the non-linearity of the inverse problem entails that solution methods are extremely sensitive to modeling errors and, even worse, are prone to the occurrence of false solutions, that is, estimates of the unknown function that match the data but are different from the ground truth. As such, most of the effective applications of tomographic GPR are so far concerned with linearized inversion approaches, which are based on simple models and are indeed free from false solutions. However, they can only provide information on the location of buried targets and not on their electromagnetic features. In this paper, we present a new tomographic approach that, being based on a “scenario specific” linearized model arising from a suitable preprocessing of the measured data, allows to both achieve information on the electromagnetic features and avoid occurrence of false solutions. The method is presented and numerically assessed for the case of cross-borehole GPR surveys.