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Andrea Randazzo

Bio: Andrea Randazzo is an academic researcher from University of Genoa. The author has contributed to research in topics: Microwave imaging & Inverse scattering problem. The author has an hindex of 27, co-authored 199 publications receiving 2217 citations.


Papers
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Journal ArticleDOI
TL;DR: The optimization of difference patterns of monopulse antennas is considered and the cost function is based on constraints on the side-lobe levels, which is efficiently solved by a differential evolution algorithm.
Abstract: 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.

146 citations

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TL;DR: 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.
Abstract: 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.

114 citations

Journal ArticleDOI
TL;DR: 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 and the performances of the method are assessed by Means of experimental results in a real scenario.
Abstract: 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.

88 citations

Journal ArticleDOI
TL;DR: In this article, a novel imaging algorithm, performing a Lp Banach-space regularization, is proposed for 2-D electromagnetic inverse scattering problems, and the reconstruction capabilities of the methods are evaluated by using numerical and experimental data.
Abstract: 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 Lp 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.

85 citations

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TL;DR: A new approach to noninvasive inspection of dielectric targets at microwave frequencies is proposed, which is aimed at assessing the capabilities of the approach in dealing with the nonlinear ill-posed inverse problem associated to the short-range microwave imaging.
Abstract: A new approach to noninvasive inspection of dielectric targets at microwave frequencies is proposed. Cylindrical dielectric objects are reconstructed under the second-order Born approximation. A multi-illumination configuration is considered. The continuous model is discretized by the moment method and an efficient inexact-Newton method is applied. The dielectric profile is iteratively reconstructed starting from the measured scattered data, which are related to the unknown target through the inverse scattering equations written in a variational setting. Several numerical results are reported, which are aimed at assessing the capabilities of the approach in dealing with the nonlinear ill-posed inverse problem associated to the short-range microwave imaging. Single, multilayer, and separate cylinders are reconstructed in noiseless and noisy environments.

72 citations


Cited by
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Journal ArticleDOI
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

4,321 citations

Journal ArticleDOI
TL;DR: A comprehensive coverage of different Differential Evolution formulations in solving optimization problems in the area of computational electromagnetics is presented, focusing on antenna synthesis and inverse scattering.
Abstract: In electromagnetics, optimization problems generally require high computational resources and involve a large number of unknowns. They are usually characterized by non-convex functionals and continuous spaces suitable for strategies based on Differential Evolution (DE). In such a framework, this paper is aimed at presenting an overview of Differential Evolution-based approaches used in electromagnetics, pointing out novelties and customizations with respect to other fields of application. Starting from a general description of the evolutionary mechanism of Differential Evolution, Differential Evolution-based techniques for electromagnetic optimization are presented. Some hints on the convergence properties and the sensitivity to control parameters are also given. Finally, a comprehensive coverage of different Differential Evolution formulations in solving optimization problems in the area of computational electromagnetics is presented, focusing on antenna synthesis and inverse scattering.

496 citations

Journal ArticleDOI
TL;DR: In this article, an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems is presented, focusing on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves.
Abstract: This review is aimed at presenting an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems. The focus of this work is on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves. Starting from a general description of the structure of EAs, the classical stochastic operators responsible for the evolution process are described. The extension to hybrid implementations when integrated with local search techniques and the exploitation of the 'domain knowledge', either a priori obtained or collected during the optimization process, are also presented. Some theoretical discussions concerned with the convergence issues and a sensitivity analysis on the parameters influencing the stochastic process are reported as well. Successively, a review on how various researchers have applied or customized different evolutionary approaches to inverse scattering problems is carried out ranging from the shape reconstruction of perfectly conducting objects to the detection of the dielectric properties of unknown scatterers up to applications to sub-surface or biomedical imaging. Finally, open problems and envisaged developments are discussed.

439 citations