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Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling
- Source :
- IEEE Transactions on Antennas and Propagation, IEEE Transactions on Antennas and Propagation, Institute of Electrical and Electronics Engineers, 2012, 60 (5), pp.2309-2326. ⟨10.1109/TAP.2012.2189742⟩
- Publication Year :
- 2012
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2012.
-
Abstract
- An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task (MT) Bayesian compressive sensing (BCS) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine (RVM) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT-BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods.
- Subjects :
- Mathematical optimization
Matching (graph theory)
Numerical analysis
020208 electrical & electronic engineering
Bayesian probability
Probabilistic logic
Constrained optimization
020206 networking & telecommunications
02 engineering and technology
[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism
Compressed sensing
0202 electrical engineering, electronic engineering, information engineering
Pattern matching
Electrical and Electronic Engineering
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
ComputingMilieux_MISCELLANEOUS
Sparse matrix
Mathematics
Subjects
Details
- ISSN :
- 15582221 and 0018926X
- Volume :
- 60
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Antennas and Propagation
- Accession number :
- edsair.doi.dedup.....14aeb77c7e30833216789252b2d74f4d