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Fast Implementation of SAR Imaging Using Sparse ML Methods

Glentis, George; Zhao, Kexin; Jakobsson, Andreas LU ; Abeida, Habti and Li, Jian (2013) 47th Annual Asilomar Conference on Signals, Systems, and Computers, 2003 In Signals, Systems and Computers, 2013 Asilomar Conference on 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)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
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
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
2014-09-26 16:13:49
date last changed
2017-01-01 07:56:47
@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},
  year         = {2013},
}