4 May 2016 A new sparse Bayesian learning method for inverse synthetic aperture radar imaging via exploiting cluster patterns
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Abstract
The application of sparse representation to SAR/ISAR imaging has attracted much attention over the past few years. This new class of sparse representation based imaging methods present a number of unique advantages over conventional range-Doppler methods, the basic idea behind these works is to formulate SAR/ISAR imaging as a sparse signal recovery problem. In this paper, we propose a new two-dimensional pattern-coupled sparse Bayesian learning(SBL) method to capture the underlying cluster patterns of the ISAR target images. Based on this model, an expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Experimental results demonstrate that the proposed method is able to achieve a substantial performance improvement over existing algorithms, including the conventional SBL method.
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Jun Fang, Lizao Zhang, Huiping Duan, Lei Huang, Hongbin Li, "A new sparse Bayesian learning method for inverse synthetic aperture radar imaging via exploiting cluster patterns", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570D (4 May 2016); doi: 10.1117/12.2225157; https://doi.org/10.1117/12.2225157
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