A review of recently introduced Bayesian approaches for the synthesis of maximally-sparse antenna arrays is presented. More specifically, the use of numerically-efficient techniques based on the Bayesian Compressive Sampling (BCS) is introduced to solve the linear array design problem. Towards this end, a probabilistic framework is exploited to formulate the synthesis problem, and a fast relevance vector machine (RVM) is employed for the computation of the optimal excitations and geometries. An illustrative numerical validation is presented to show the features of the proposed approach.

BCS-Based Formulations for Antenna Arrays Synthesis

Oliveri, Giacomo;Carlin, Matteo;Massa, Andrea
2012-01-01

Abstract

A review of recently introduced Bayesian approaches for the synthesis of maximally-sparse antenna arrays is presented. More specifically, the use of numerically-efficient techniques based on the Bayesian Compressive Sampling (BCS) is introduced to solve the linear array design problem. Towards this end, a probabilistic framework is exploited to formulate the synthesis problem, and a fast relevance vector machine (RVM) is employed for the computation of the optimal excitations and geometries. An illustrative numerical validation is presented to show the features of the proposed approach.
2012
6th European Conference on Antennas and Propagation (EUCAP) 2012
Prague, CZ
IEEE
Oliveri, Giacomo; Carlin, Matteo; Massa, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/91699
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