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Proceedings Paper

SAR imaging via iterative adaptive approach and sparse Bayesian learning
Author(s): Ming Xue; Enrique Santiago; Matteo Sedehi; Xing Tan; Jian Li
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Paper Abstract

We consider sidelobe reduction and resolution enhancement in synthetic aperture radar (SAR) imaging via an iterative adaptive approach (IAA) and a sparse Bayesian learning (SBL) method. The nonparametric weighted least squares based IAA algorithm is a robust and user parameter-free adaptive approach originally proposed for array processing. We show that it can be used to form enhanced SAR images as well. SBL has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown in the literature that SBL is easy to use and can recover sparse signals more accurately than the l 1 based optimization approaches, which require delicate choice of the user parameter. We consider using a modified expectation maximization (EM) based SBL algorithm, referred to as SBL-1, which is based on a three-stage hierarchical Bayesian model. SBL-1 is not only more accurate than benchmark SBL algorithms, but also converges faster. SBL-1 is used to further enhance the resolution of the SAR images formed by IAA. Both IAA and SBL-1 are shown to be effective, requiring only a limited number of iterations, and have no need for polar-to-Cartesian interpolation of the SAR collected data. This paper characterizes the achievable performance of these two approaches by processing the complex backscatter data from both a sparse case study and a backhoe vehicle in free space with different aperture sizes.

Paper Details

Date Published: 28 April 2009
PDF: 12 pages
Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 733706 (28 April 2009); doi: 10.1117/12.817781
Show Author Affiliations
Ming Xue, Univ. of Florida (United States)
Enrique Santiago, Univ. of Florida (United States)
Matteo Sedehi, Univ. of Rome, La Sapienza (Italy)
Xing Tan, Univ. of Florida (United States)
Jian Li, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 7337:
Algorithms for Synthetic Aperture Radar Imagery XVI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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