Fast Implementation of SAR Imaging Using Sparse ML Methods
(2013) 47th Annual Asilomar Conference on Signals, Systems, and Computers, 2003 p.922-926- Abstract
- High-resolution sparse spectral estimation techniques have recently been shown to offer significant performance gains as compared to most conventional estimation approaches, although such methods typically suffer the drawback of being computationally cumbersome. In this paper, we seek to alleviate this drawback somewhat, examining computationally efficient implementations of the recent iterative sparse maximum likelihood-based approaches (SMLA), exploiting the inherent rich structure of these estimators. The derived implementations reduce the resulting computational complexity with at least one order of magnitude, while still yielding exact implementations. The effectiveness of the discussed techniques are illustrated using experimental... (More)
- High-resolution sparse spectral estimation techniques have recently been shown to offer significant performance gains as compared to most conventional estimation approaches, although such methods typically suffer the drawback of being computationally cumbersome. In this paper, we seek to alleviate this drawback somewhat, examining computationally efficient implementations of the recent iterative sparse maximum likelihood-based approaches (SMLA), exploiting the inherent rich structure of these estimators. The derived implementations reduce the resulting computational complexity with at least one order of magnitude, while still yielding exact implementations. The effectiveness of the discussed techniques are illustrated using experimental examples. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4645526
- author
- Glentis, George ; Zhao, Kexin ; Jakobsson, Andreas LU ; Abeida, Habti and Li, Jian
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Signals, Systems and Computers, 2013 Asilomar Conference on
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 47th Annual Asilomar Conference on Signals, Systems, and Computers, 2003
- conference location
- Pacific Grove, CA, United States
- conference dates
- 2003-11-03 - 2003-11-06
- external identifiers
-
- scopus:84901262016
- ISBN
- 978-1-4799-2388-5 (Print)
- DOI
- 10.1109/ACSSC.2013.6810423
- language
- English
- LU publication?
- yes
- id
- ed131898-37d0-49ed-9d3f-f87b3fc3dbc0 (old id 4645526)
- alternative location
- http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6810423
- date added to LUP
- 2016-04-04 10:13:27
- date last changed
- 2022-01-29 19:59:06
@inproceedings{ed131898-37d0-49ed-9d3f-f87b3fc3dbc0, abstract = {{High-resolution sparse spectral estimation techniques have recently been shown to offer significant performance gains as compared to most conventional estimation approaches, although such methods typically suffer the drawback of being computationally cumbersome. In this paper, we seek to alleviate this drawback somewhat, examining computationally efficient implementations of the recent iterative sparse maximum likelihood-based approaches (SMLA), exploiting the inherent rich structure of these estimators. The derived implementations reduce the resulting computational complexity with at least one order of magnitude, while still yielding exact implementations. The effectiveness of the discussed techniques are illustrated using experimental examples.}}, author = {{Glentis, George and Zhao, Kexin and Jakobsson, Andreas and Abeida, Habti and Li, Jian}}, booktitle = {{Signals, Systems and Computers, 2013 Asilomar Conference on}}, isbn = {{978-1-4799-2388-5 (Print)}}, language = {{eng}}, pages = {{922--926}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Fast Implementation of SAR Imaging Using Sparse ML Methods}}, url = {{http://dx.doi.org/10.1109/ACSSC.2013.6810423}}, doi = {{10.1109/ACSSC.2013.6810423}}, year = {{2013}}, }