The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Reliable Diagnosis of Large Linear Arrays—A Bayesian Compressive Sensing Approach
Abstract
An innovative array diagnosis technique based on a compressive-sensing (CS) paradigm is introduced in the case of linear arrangements. Besides detecting the faulty elements, the approach is able to provide the degree of reliability of such an estimation. Starting from the measured samples of the far-field pattern, the array diagnosis problem is formulated in a Bayesian framework and it is successively solved with a fast relevance vector machine (RVM). The arising Bayesian compressive sensing (BCS) approach is numerically validated through a set of representative examples aimed at providing suitable user's guidelines as well as some insights on the method features and potentialities.
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Citations
Cites
An Introduction To Compressive Sampling
Candes, E. J., & Wakin, M. B. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914731 · Cited by 10,017
Cited by
Compressive Sensing in Electromagnetics - A Review
Massa, A., Rocca, P., & Oliveri, G. (2015) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2015.2397092 · Cited by 360
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Metadata
- Authors
- Oliveri, G., Rocca, P., & Massa, A.
- Venue
- IEEE Transactions on Antennas and Propagation
- Published
- 2012-10
- Volume
- 60
- Issue
- 10
- Pages
- 4627-4636
- ISSN
- 0018-926X (ISSN-L)
- 1558-2221
- OpenAlex
- W2085637236
- Cited by
- 169
- References
- 1
- FWCI
- 13.66
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
