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Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing strategy. The problem, formulated in a probabilistic fashion as a pattern-matching synthesis, is that of determining the sparsest excitation set (locations and weights) fitting a reference pattern subject to user-defined geometrical constraints. Results from a set of representative numerical experiments are presented to illustrate the key-features of the proposed approach as well as to assess, also through comparisons, its potentials in terms of matching accuracy, element saving, and computational costs.

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The array miniaturization problem is addressed by means of a Material-by-Design approach. More specifically, an innovative strategy that integrates a Quasi-Conformal Transformation Optics (QCTO) technique and a Source Inversion method is proposed to design radome-coated arrays exhibiting the same radiation properties of wider virtual arrangements comprising more antenna elements. Towards this end, the state-of-the-art QCTO theory is generalized to account for source constraints within the synthesis process. Representative numerical examples are provided to assess the reliability, the flexibility, and the effectiveness of the proposed synthesis approach as well as the possibility to realize sub-optimal radomes with simplified, but cheaper/easier, structures (e.g., structures based on tiles of isotropic dielectrics).