eprintid: 505
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/05/05
datestamp: 2011-06-30
lastmod: 2013-06-30 08:42:42
status_changed: 2013-06-30 08:42:42
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Oliveri, Giacomo
creators_name: Massa, Andrea
title: Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays
ispublished: pub
subjects: TU
full_text_status: public
keywords: Array synthesis , Bayesian compressive sampling (BCS) , linear arrays , relevance vector machine , sparse arrays
abstract: A numerically-efficient technique based on the Bayesian compressive sampling (BCS) for the design of maximally-sparse linear arrays is introduced. The method is based on a probabilistic formulation of the array synthesis and it exploits a fast relevance vector machine (RVM) for the problem solution. The proposed approach allows the design of linear arrangements fitting desired power patterns with a reduced number of non-uniformly spaced active elements. The numerical validation assesses the effectiveness and computational efficiency of the proposed approach as a suitable complement to existing state-of-the-art techniques for the design of sparse arrays. “(c) 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.”
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: TRUE
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citation:   Oliveri, Giacomo and Massa, Andrea  (2011) Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/505/1/DISI-11-103-R197.pdf