Andrea Massa

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.

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.

TL;DR: 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.

TL;DR: A review of the state-of-the-art and most recent advances of compressive sensing and related methods as applied to electromagnetics can be found in this article, where a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed.

TL;DR: A numerically-efficient technique based on the Bayesian compressive sampling (BCS) for the design of maximally-sparse linear arrays is introduced, based on a probabilistic formulation of the array synthesis and it exploits a fast relevance vector machine for the problem solution.