Mr. Enrique A. Santiago Profile

Enrique A. Santiago

at Univ of Florida

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Proceedings Article | 29 April 2009 Paper

SAR imaging via iterative adaptive approach and sparse Bayesian learning

Ming Xue, Enrique Santiago, Matteo Sedehi, Xing Tan, Jian Li

Proceedings Volume 7337, 733706 (2009) https://doi.org/10.1117/12.817781

KEYWORDS: Synthetic aperture radar, Expectation maximization algorithms, Data acquisition, Space based lasers, Image resolution, Data modeling, Electroluminescence, Resolution enhancement technologies, Backscatter, Radar

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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.

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