Support Detection for SAR Tomographic Reconstructions from Compressive Measurements
Joint Authors
Schirinzi, Gilda
Budillon, Alessandra
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-01
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The problem of detecting and locating multiple scatterers in multibaseline Synthetic Aperture Radar (SAR) tomography, starting from compressive measurements and applying support detection techniques, is addressed.
Different approaches based on the detection of the support set of the unknown sparse vector, that is, of the position of the nonzero elements in the unknown sparse vector, are analyzed.
Support detection techniques have already proved to allow a reduction in the number of measurements required for obtaining a reliable solution.
In this paper, a support detection method, based on a Generalized Likelihood Ratio Test (Sup-GLRT), is proposed and compared with the SequOMP method, in terms of probability of detection achievable with a given probability of false alarm and for different numbers of measurements.
American Psychological Association (APA)
Budillon, Alessandra& Schirinzi, Gilda. 2015. Support Detection for SAR Tomographic Reconstructions from Compressive Measurements. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-6.
https://search.emarefa.net/detail/BIM-1079318
Modern Language Association (MLA)
Budillon, Alessandra& Schirinzi, Gilda. Support Detection for SAR Tomographic Reconstructions from Compressive Measurements. The Scientific World Journal No. 2015 (2015), pp.1-6.
https://search.emarefa.net/detail/BIM-1079318
American Medical Association (AMA)
Budillon, Alessandra& Schirinzi, Gilda. Support Detection for SAR Tomographic Reconstructions from Compressive Measurements. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-6.
https://search.emarefa.net/detail/BIM-1079318
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1079318