The problem of synthesizing maximally-sparse linear arrays with complex excitations is solved through a numericallyefficient approach based on the Bayesian Compressive Sampling (BCS). The array design problem is re-cast in a probabilistic framework, and a fast relevance vector machine (RVM) is employed for the computation of the optimal layout and associated complex weights. A preliminary numerical validation is presented to assess the potentialities and limitations of the proposed approach.

A CS-based Strategy for the Design of Shaped-beam Sparse Arrays

Oliveri, Giacomo;Carlin, Matteo;Massa, Andrea
2011-01-01

Abstract

The problem of synthesizing maximally-sparse linear arrays with complex excitations is solved through a numericallyefficient approach based on the Bayesian Compressive Sampling (BCS). The array design problem is re-cast in a probabilistic framework, and a fast relevance vector machine (RVM) is employed for the computation of the optimal layout and associated complex weights. A preliminary numerical validation is presented to assess the potentialities and limitations of the proposed approach.
2011
IEEE International Symposium on Antennas and Propagation
Piscataway, NJ
IEEE
9780769544632
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/11572/89728
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