SAR imaging via iterative adaptive approach and sparse Bayesian learning

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

doi:10.1117/12.817781

28 April 2009 SAR imaging via iterative adaptive approach and sparse Bayesian learning

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

Proceedings Volume 7337, Algorithms for Synthetic Aperture Radar Imagery XVI; 733706 (2009) https://doi.org/10.1117/12.817781
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Ming Xue, Enrique Santiago, Matteo Sedehi, Xing Tan, and Jian Li "SAR imaging via iterative adaptive approach and sparse Bayesian learning", Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 733706 (28 April 2009); https://doi.org/10.1117/12.817781

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