| Title | Non-Parametric High-Resolution SAR Imaging |
| Authors | George-Othan Glentis, Kexin Zhao, Andreas Jakobsson, Jian Li |
| Alternative Location | http://dx.doi.org/10.1109/T..., Restricted Access |
| Publication | IEEE Transactions on Signal Processing |
| Year | 2013 |
| Document type | Article |
| Status | Inpress |
| Quality controlled | Yes |
| Language | eng |
| Publisher | IEEE |
| Abstract English | The development of high-resolution two-dimensional spectral estimation techniques is of notable<br> interest in synthetic aperture radar (SAR) imaging. Typically, data-independent techniques are exploited<br> to form the SAR images, although such approaches will suffer from limited resolution and high sidelobe<br> levels. Recent work on data-adaptive approaches have shown that both the iterative adaptive approach<br> (IAA) and the sparse learning via iterative minimization (SLIM) algorithm offer excellent performance<br> with high-resolution and low side lobe levels for both complete and incomplete data sets. Regrettably,<br> both algorithms are computationally intensive if applied directly to the phase history data to form the<br> SAR images. To help alleviate this, efficient implementations have also been proposed. In this paper,<br> we further this work, proposing yet further improved implementation strategies, including approaches<br> using the segmented IAA approach and the approximative quasi-Newton technique. Furthermore, we<br> introduce a combined IAA-MAP algorithm as well as a hybrid IAA- and SLIM-based estimation scheme<br> for SAR imaging. The effectiveness of the SAR imaging algorithms and the computational complexities<br> of their fast implementations are demonstrated using the simulated Slicy data set and the experimentally<br> measured GOTCHA data set. |
| Keywords | synthetic aperture radar imaging, Spectral estimation, data adaptive techniques, efficient algorithms, |
| ISBN/ISSN/Other | ISSN: 1053-587X (print) |
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Last update: 2013-04-11
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