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
File in questo prodotto:
File Dimensione Formato  
C234.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 216.67 kB
Formato Adobe PDF
216.67 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/89728
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
  • OpenAlex ND
social impact