P. Rocca

Optimization of metamaterial WAIM for planar arrays

Summary form only given. For wide-angle scanning planar arrays, the magnitude of the reflection coefficient may change substantially with scan angle, scan plane and wave polarization. The conventional solution to this problem consists in the use of wide-angle impedance matching (WAIM) structures made up of a stack of dielectric layers. Much greater flexibility may be achieved by employing anisotropic slabs with controllable spatial dispersivity. Artificially structured materials (or metamaterials) make this approach feasible by allowing the simultaneous control of dielectric and magnetic properties in the different directions (S.Sajuyigb et al., IET Microw. Antennas Propag., 4(8), 1063-1072, 2010).

On the integration of smart antennas in Wireless Sensor Networks

This paper presents a preliminary assessment of the potentialities of the exploitation of smart antennas in WSNs. The more trivial benefits coming from such an integration are: (a) an efficient spatial management of the radiated energy and (b) an efficient and adaptive reuse of wireless links in order to increase the network throughput and to solve coexistence problems arising from the integration with other wireless technologies. However, in order to take advantage of a smart integration at the physical layer, significant improvements both in flexibility and size of the adaptive antenna on the WSN node are needed.

Evolutionary strategies for advanced array optimization

Evolutionary algorithms (EAs) have had a widespread diffusion and have demonstrated to be very effective techniques for the design of antenna arrays. However, the main limitations are the high computational burden and low convergence speed which stimulated researchers towards the development of even more effective approaches or the definition of suitable optimization frameworks. Two innovative EA-based method for the synthesis of large thinned arrays and of compromise sum-difference sub-arrayed antennas are presented and discussed in this work where the flexibility and performance of EAs have been exploited to obtain effective and efficient designs.

Imaging complex targets through alphabet-based compressive sensing

Imaging techniques exploiting sparseness-regularized formulations emerged in the last few years as powerful and effective retrieval methods in several heterogeneous scenarios [1] including structural monitoring, non-destructive testing and evaluation, ground penetrating radar imaging, and biomedical diagnosis [2-8]. The success of the this class of algorithms, which are often collectively indicated as Compressive Sensing (CS) [9], is motivated by several concurring factors, including their accuracy, robustness, numerical efficiency, capability to handle several different contexts (e.g., including transverse-magnetic [2] and transverse-electric problems [3], single-/multi-frequency data [2, 4], isotropic/anisotropic media [1]) in a seamless way, and the availability of efficient implementations of many different solvers [1]. On the other hand, CS techniques are not general-purpose imaging algorithms. Their application requires the problem at hand to comply with some fundamental assumptions [1], including the fact that the unknown (e.g., the contrast [8] or equivalent currents [9]) is sparse in the employed basis. Early applications of CS methods, which used simple pixel-bases to expand the unknowns, addressed only those scenarios were the object is composed by few isolated pixels [2]. Nevertheless, it is well known that sparsity is not an absolute concept, but it is always in relation with the representation basis [1]. The use of different expansion bases [10] has been then proposed to enable more complex profiles to be imaged [7]. Unfortunately, such techniques always assume that some knowledge on the target profile is available, so that this a-priori information can be exploited to define the most suitable expansion basis to be adopted [7]. A different perspective is considered in this paper to enable the application of CS methodologies when no prior information on the class of targets under investigation is available. More specifically, a large alphabet of candidate bases is generated off-line, and for each basis a candidate reconstructions are carried out in parallel “online” by the CS retrieval tool. The retrieved profiles are then compared, and a sparsity-based criterion is adopted in turns to select the most reliable reconstruction (among those obtained by the different bases). Preliminary numerical experiments will be shown to assess the accuracy and numerical efficiency of the proposed imaging methodology.

An innovative optimization strategy for phased array tiling

A novel approach for phased array tiling is presented in this work. The method allows finding domino tiling configurations that are optimal in terms of radiation performance of the corresponding clustered layout. A preliminary numerical example is reported to illustrate the sidelobe suppression enabled by the proposed clustering strategy.

Optimization-based design of innovative grating-lobe free radar UWB architectures

An innovative approach for reducing grating lobes appearing when the inter-element spacing of an UWB linear array is greater than half-wavelength is presented. The main innovation of the proposed new design methodology is related to the fact that the positions of the array elements are left unchanged in the direction of the axis of the array (thus not affecting the gain of the pattern), while they are optimized along broadside direction. Selected numerical results validate the proposed methodology.

Long-distance WPT unconventional arrays synthesis

Abstract Two innovative array concepts are introduced for the design of long-distance Wireless Power Transfer (WPT) radiating systems. The achievable tradeoffs between complexity/cost mitigation and power focusing capabilities of unconventional WPT architectures with respect to state-of-the-art optimal WPT solutions are investigated. To this end, clustered or sparse WPT arrangements are introduced by formulating their syntheses either as excitation or as pattern matching problems then solved by ad-hoc versions of the Contiguous Partition Method and Compressive Sensing algorithms. Selected numerical examples are presented to assess the features and the potentialities of unconventional WPT designs also in comparison with traditional state-of-the-art optimal methods.

Pulse sequence optimization in time-modulated arrays for secure communications

The sideband radiations (SR) generated in time-modulated linear arrays (TMLAs) because of the periodicity of the modulating pulse sequence are exploited for secure communication purposes. The pulse sequence controlling the on/off status of the array elements is optimized by means of a binary Genetic Algorithm (GA) in order to maximize the distortion of the signal transmitted by the array along undesired directions while keeping it unaltered along the angular region of interest.

Compressive sensing as a new paradigm in wave scattering and propagation

The paradigm of Compressive Sensing (CS) has emerged in the last few years as a flexible and powerful methodological strategy to address synthesis and analysis problems arising in wave scattering and propagation engineering. The success of CS techniques is motivated by several factors, including (i) their capability to address sampling/recovery problems overcoming the classical Nyquist/Shannon limits, (ii) their flexibility and ease of adaptation to several different scenarios, including array design, direction-of-arrival estimation, microwave and radar imaging, (iii) the availability of powerful, effective, and numerically efficient implementations of CS sampling and retrieval algorithms. Accordingly, and despite their relatively recent introduction, the research on the development, application, generalization, and customization of CS techniques has already become one of the most active areas within wave scattering and propagation engineering. Within this framework, this invited paper is aimed at illustrating the fundamental features of Compressive Sensing as well as at discussing its potential applicability in wave scattering and propagation problems also through a review of the recent advances in the state-of-the-art concerning CS. Some current research trends and open challenges will be also discussed.

Computationally-efficient analytic techniques for large array synthesis - feasibility and real-implementations

In this paper the features and potentialities of real implementations of analytically thinned arrays based on Almost Difference Sets (ADSs) are analyzed. Towards this end, a preliminary numerical analysis is presented to assess the peak sidelobe level (PSL) behavior of analytically designed layouts including mutual coupling effects and possibly damaged elements. A set of preliminary design guidelines is deduced to enable the practical exploitation of analytically thinned arrangements.