Electromagnetic skins (EMSs) have been recently considered as a booster for wireless sensing, but their usage on mobile targets is relatively novel and could be of interest when the target reflectivity can/must be increased to improve its detection or the estimation of parameters. In particular, when illuminated by a wide-bandwidth signal (e.g., from a radar operating at millimeter waves), vehicles behave like extended targets, since multiple parts of the vehicle’s body effectively contribute to the back-scattering. Moreover, in some cases perspective deformations challenge the correct localization of the vehicle. To address these issues, we propose lodging EMSs on vehicles’ roof to act as high-reflectivity planar retro-reflectors toward the sensing terminal. The advantage is twofold: 1) by introducing a compact high-reflectivity structure on the target, we make vehicles behave like point targets, avoiding perspective deformations and related ranging biases and 2) we increase the reflectivity of the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wave-level considering both reconfigurable intelligent surfaces (RIS) and cost-effective static passive electromagnetic skins (SP-EMSs). Localization performance of the EMS-aided sensing system is also assessed by Cramér-Rao bound analysis in both narrowband and spatial wideband operating conditions.

An iterative multiscaling approach for solving the electromagnetic inverse scattering problem related to the imaging of shallow subsurface targets with the ground-penetrating radar (GPR) is proposed. The approach combines the zooming properties of the multiscaling technique with the reconstruction capabilities of an Inexact-Newton (IN) method developed in$L^{p}$spaces. It is based on multifrequency processing that allows one to face the ill-posedness of the inverse scattering problem by exploiting the regularization properties of a truncated Landweber (LW) method. Experimental data, extracted from radargrams obtained by the GPR in a real situation, are used for validation. The reconstruction results are also compared with those from competitive alternatives, such as a standard IN method or a state-of-the-art multifrequency Conjugate Gradient (CG)-based approach.

Compressive sensing (CS) is currently one the most active research field in information engineering and science. The CS features/properties (e.g., its flexibility, robustness, accuracy, and effectiveness) and the strong theoretical background have motivated a great interest in developing and applying CS to many domains including antenna arrays. Indeed, several problems arising in such a framework can be directly formulated or suitably recast for an effective solution within the CS environment. This work is aimed at presenting a review of the current and most recent advances of CS formulations for antenna arrays developed at the ELEDIA research center. Towards this end, a set of representative applicative scenarios are illustrated ranging from the diagnosis and synthesis of antenna arrays up to antenna array processing including the estimation of directions-of-arrival. Current challenges and trends in the application of CS to the solution of traditional and/or innovative antenna array problems are discussed, as well.

The proliferation of wireless services is driving innovative phased array solutions that are able to provide better cost/performance tradeoffs. In this context, the use of irregular array architectures provides a viable solution. This paper reviews and highlights some of the most recent advances in this field, including clustered, thinned, sparse, and time-modulated arrays, and their proposed design methodologies.

The real-time retrieval of the characteristics of a defect with eddy current testing in a nondestructive testing and evaluation framework is addressed. An innovative statistical learning approach is developed to deal with the inversion problem at hand in a computationally efficient way. More in detail, a feature extraction technique based on partial least squares (PLS) is profitably combined with a customized output space filling (OSF) adaptive sampling scheme for generating optimal training databases, while accurate and robust reconstructions are performed with a support vector regression (SVR) algorithm. A selected set of numerical and experimental results is reported to assess the effectiveness as well as the efficiency of the proposed PLS-OSF/SVR approach.

The introduction of electromagnetic (EM) media with unique field manipulation properties, collectively labelled as metamaterials, has boosted the interest toward the design, the fabrication, and the testing of artificial materials whose features can be engineered according to the applicative requirements and user objectives. In this framework, the long-term promise of reconfigurable metamaterial theory (i.e., the possibility to change the EM response of a 2-D/3-D material arbitrarily and in real time) has given the designers an extremely wide number of new degrees of freedom for the synthesis of innovative adaptive systems. Moreover, successful experimental validations of reconfigurable metamaterials in the entire EM spectrum from microwaves to optical frequencies have further stimulated academic and industrial interests in developing devices with enhanced performances, efficiency, and robustness. Nevertheless, the exploitation of reconfigurable metamaterials in commercial devices is still an open problem with several challenges from both the theoretical and technological viewpoints. This paper is then aimed at reviewing the latest advances on reconfigurable metamaterial engineering from the methodological perspective also providing a comprehensive and balanced survey on latest concepts, current trends, and envisaged future developments on this active field of research.

An innovative information-acquisition approach to 2-D ground-penetrating radar (GPR) prospecting is presented. A microwave inverse-scattering nested scheme combining a frequency hopping (FH) procedure and a multifocusing (MF) technique is proposed. On the one hand, the FH scheme effectively handles multifrequency GPR data, whereas on the other hand, MF techniques have been proven to be effective tools in reducing the occurrence of multilocal minima affecting GPR investigations. This allows the use of a local search technique based on the conjugate gradient method to iteratively solve the inverse problem at hand. Selected results are reported and analyzed to give some insights to the interested readers on the advantages and limitations of such an approach when handling synthetically generated and experimental GPR data.

The concept of long-range wireless power transmission (WPT) has been formulated shortly after the invention of high power microwave amplifiers. The promise of WPT, energy transfer over large distances without the need to deploy a wired electrical network, led to the development of landmark successful experiments, and provided the incentive for further research to increase the performances, efficiency, and robustness of these technological solutions. In this framework, the key-role and challenges in designing transmitting and receiving antenna arrays able to guarantee high-efficiency power transfer and cost-effective deployment for the WPT system has been soon acknowledged. Nevertheless, owing to its intrinsic complexity, the design of WPT arrays is still an open research field whose importance is growing as the possibility to transfer energy by means of electromagnetic waves gathers more and more interest from the applicative viewpoint. This paper is aimed at reviewing the array design approaches proposed in the state of the art for long-range wireless power transmission, highlighting the latest advances and innovative solutions as well as envisaging possible future trends of the research in this area.

Application of wireless technologies in the smart home is dealt with by pointing out advantages and limitations of available approaches for the solution of heterogeneous and coexisting problems related to the distributed monitoring of the home and the inhabitants. Some hot challenges facing the exploitation of noninvasive wireless devices for user behavior monitoring are then addressed and the application fields of smart power management and elderly people monitoring are chosen as representative cases where the estimation of user activities improves the potential of location-aware services in the smart home. The problem of user localization is considered with great care to minimize the invasiveness of the monitoring system. Wireless architectures are reviewed and discussed as flexible and transparent tools toward the paradigm of a totally automatic/autonomic environment. With respect to available state-of-the-art solutions, our proposed architecture is based also on existing wireless devices and exploits, in an opportunistic way, the characteristics of wireless signals to estimate the presence, the movements, and the behaviors of inhabitants, reducing the system complexity and costs. Selected and representative examples from real implementations are presented to give some insight on state-of-the-art solutions also envisaging possible future trends.

In this paper, the reconstruction of sparse scatterers under multiview transverse-electric illumination is dealt with. Starting from a probabilistic formulation of the “inverse source” problem, two Bayesian compressive sensing approaches are introduced. The former is a suitable extension of the single-task method presented earlier for the transverse-magnetic scalar case, while the other exploits an innovative multitask implementation to take into account the relationships among the “contrast currents” at the different probing views. Representative numerical results are discussed to assess, also comparatively, the numerical efficiency, the accuracy, and the robustness of the proposed approaches.

We consider cross-borehole imaging of buried objects embedded in a large search area by using a time domain inverse analysis. For simplicity, a two-dimensional model is examined. In order to avoid trap into false solution and enhance the achievable spatial resolution, the iterative multiscaling strategy combined with the forward-backward time-stepping method is proposed. Preliminary results show the effectiveness of proposed method.

In this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions.

The detection of buried landmines or unexploded ordnances is a problem of great interest in both civilian and military applications. In such a framework, this paper presents an approach for real-time localization of unknown subsurface objects. Such a technique is aimed at defining high resolution risk maps by means of a classification strategy based on a support vector machine and a multi-resolution approach. In order to assess its effectiveness, a selected set of numerical results regarding multiple target scenarios are presented.

In this paper the problem of detecting the presence of similar OFDM signals, i.e. WLAN and WiMAX signals, in an Open Spectrum scenario is faced. The identification of the channel occupancy and the signal classification are performed by using a fast detector based on a single spectral correlation function estimator and a multi-class support vector machine classifier which are designed and tested in a multipath environment. Finally, the obtained numerical results and the amount of processing necessary to perform the considered operations are reported and discussed.