The optimization of difference patterns of monopulse antennas is considered. The synthesis problem is recast as an optimization problem by defining a suitable cost function. In particular, in this paper, the cost function is based on constraints on the side-lobe levels. A subarray configuration is adopted and the excitations of the difference pattern are approximately determined. The optimization problem is efficiently solved by a differential evolution algorithm, which is able to contemporarily handle real and integer unknowns. Numerical results are reported concerning classical array configurations previously considered in the literature.

In this paper, the use of a smart antenna system for the estimation of the directions of arrival (DOAs) of multiple waves is considered. An efficient method based on the support vector regression is proposed, in which the mapping among the outputs of the array and the DOAs of unknown plane waves is approximated by means of a family of support vector machines. Several numerical results are provided for the validation of the proposed approach, considering multiple impinging waves both in noiseless and noisy environments.

In this letter, the problem of estimating the directions of arrival (DOAs) of coherent electromagnetic waves impinging upon a uniform linear array (ULA) is considered. In particular, an efficient DOA estimation approach based on the support vector regression is assessed using experimental measurements. Moreover, the obtained results are compared with those yielded by the multiple signal classification (MUSIC) algorithm.

Stroke identification by means of microwave tomography requires a very accurate reconstruction of the dielectric properties inside patient's head. This is possible when a precise measurement system is combined with a full nonlinear inversion method. In this article, the inversion of S-parameter data collected in a metallic chamber is performed with a nonlinear inversion strategy in Lebesgue spaces with nonconstant exponents. This is the first time that this kind of nonlinear S-parameter electromagnetic formulation has been applied to this problem. The inverse-scattering method incorporates a 2-D electromagnetic model of the imaging chamber based on a finite-element formulation, which has led to a complete redefinition of the solving procedure with respect to previous works. This allows a suitable description of the multistatic S-parameters due to the interactions between the incident radiation and the structure under test. The developed inversion procedure is first assessed by means of numerical simulations. The experimental results, obtained with a clinical microwave system prototype containing a liquid-filled 3-D SAM phantom with an inhomogeneity mimicking a hemorrhagic stroke, further prove the effectiveness of the proposed approach.

Indoor localization of targets by using electromagnetic waves has attracted a lot of attention in the last few years. Thanks to the wide availability of electromagnetic sources deployed for various applications (e.g., WiFi), nowadays it is possible to perform this task by using low-cost mobile devices, such as smartphones. To this end, in order to achieve high positioning accuracy and reduce the computational resources used in the position estimation, fingerprinting approaches are usually employed. However, in this case, a time-consuming training phase, where a great number of measurements must be performed, is needed. In this letter, a novel approach, where the training data are obtained by means of finite-difference time-domain (FDTD) simulations of the electromagnetic propagation in the considered scenario, is presented. The performances of the method are assessed by means of experimental results in a real scenario.

Inverse problems arising in microwave imaging suffer from high ill-posedness. As it is well known, it is necessary to employ regularized inversion methods, in order to mitigate such behavior. Usually, such approaches are formulated in standard Hilbert spaces. Recently, a more generic regularization theory, working in Banach spaces, has been investigated, in order to overcome some limitations of the Hilbert-space regularization. In this paper, a novel imaging algorithm, performing a L^p Banach-space regularization, is proposed for 2-D electromagnetic inverse scattering problems. The reconstruction capabilities of the methods are evaluated by using numerical and experimental data.