A BCS-based approach for the synthesis of conformal arrays

Author

Oliveri, G. ; Carlin, M. ; Bekele, Ephrem T. ; Massa, A.

Author_Institution

DISI, Univ. of Trento, Trento, Italy

fYear

2013

fDate

8-12 April 2013

Firstpage

1084

Lastpage

1086

Abstract

An innovative conformal array synthesis approach is proposed which exploits a generalization of the Bayesian Compressive Sampling (BCS) technique. Towards this end, the design problem is mathematically formulated in terms of a Bayesian learning one with sparseness priors. The arising functional is then solved by means of a suitable Relevance Vector Machine (RVM) technique. Numerical results are reported to assess the effectiveness of the proposed approach in the synthesis of conformal sparse arrays.

Keywords

antenna arrays; belief networks; compressed sensing; conformal antennas; learning (artificial intelligence); BCS-based approach; Bayesian compressive sampling technique; Bayesian learning; RVM technique; conformal sparse arrays; innovative conformal array synthesis approach; relevance vector machine technique; Antenna arrays; Bayes methods; Compressed sensing; Optimized production technology; Support vector machines; Bayesian Compressive Sampling; Conformal Arrays; Relevance Vector Machine; Sparse Arrays;

fLanguage

English

Publisher

ieee

Conference_Titel

Antennas and Propagation (EuCAP), 2013 7th European Conference on

Conference_Location

Gothenburg

Print_ISBN

978-1-4673-2187-7

Electronic_ISBN

978-88-907018-1-8

Type

conf

Filename

6546451