SAR imaging via iterative adaptive approach and sparse Bayesian learning

ABSTRACT We consider sidelobe reduction and resolution enhancement in synthetic aperture radar (SAR) imaging via aniterative adaptive approach (IAA) and a sparse Bayesian learning (SBL) method. The nonparametric weightedleast squares based IAA algorithm is a robust and user parameter-free adaptive approach originally proposedfor array processing. We show that it can be used to form enhanced SAR images as well. SBL has been used asa sparse signal recovery algorithm for compressed sensing. It has been shown in the literature that SBL is easyto use and can recover sparse signals more accurately than the l 1 based optimization approaches, which requiredelicate choice of the user parameter. We consider usin g a modi“ed expectation maximization (EM) based SBLalgorithm, referred to as SBL-1, which is based on a three-stage hierarchical Bayesian model. SBL-1 is not onlymore accurate than benchmark SBL algorithms, but also converges faster. SBL-1 is used to further enhancethe resolution of the SAR images formed by IAA. Both IAA and SBL-1 are shown to be eective, requiringonly a limited number of iterations, and have no need for p olar-to-Cartesian interpo lation of the SAR collecteddata. This paper characterizes the achievable performance of these two approaches by processing the complexbackscatter data from both a sparse case study and a backho e vehicle in free space with dierent aperture sizes.Keywords: Synthetic aperture radar (SAR), wide angle SAR, iterative adaptive approach (IAA), sparseBayesian learning (SBL), side-lobe reduction, resolution enhancement.