SAR Tomography Imaging Using Sparse Bayesian Learning
Summary :
Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.1 pp.354-357
- Publication Date
- 2012/01/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.354
- Type of Manuscript
- LETTER
- Category
- Sensing
Authors
Keyword
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Rui MIN, Yating HU, Yiming PI, Zongjie CAO, "SAR Tomography Imaging Using Sparse Bayesian Learning" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 1, pp. 354-357, January 2012, doi: 10.1587/transcom.E95.B.354.
Abstract: Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.354/_p
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@ARTICLE{e95-b_1_354,
author={Rui MIN, Yating HU, Yiming PI, Zongjie CAO, },
journal={IEICE TRANSACTIONS on Communications},
title={SAR Tomography Imaging Using Sparse Bayesian Learning},
year={2012},
volume={E95-B},
number={1},
pages={354-357},
abstract={Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.},
keywords={},
doi={10.1587/transcom.E95.B.354},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - SAR Tomography Imaging Using Sparse Bayesian Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 354
EP - 357
AU - Rui MIN
AU - Yating HU
AU - Yiming PI
AU - Zongjie CAO
PY - 2012
DO - 10.1587/transcom.E95.B.354
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2012
AB - Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.
ER -
English
