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.

A BCS-based Approach for the Synthesis of Conformal Arrays

Oliveri, Giacomo;Carlin, Matteo;Bekele, Ephrem Teshale;Massa, Andrea
2013-01-01

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.
2013
7th European Conference on Antennas and Propagation (EuCAP)
Gothenburg
IEEE
Oliveri, Giacomo; Carlin, Matteo; Bekele, Ephrem Teshale; Massa, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/35068
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