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Proceedings Paper

A new sparse Bayesian learning method for inverse synthetic aperture radar imaging via exploiting cluster patterns
Author(s): Jun Fang; Lizao Zhang; Huiping Duan; Lei Huang; Hongbin Li
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Paper 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.

Paper Details

Date Published: 4 May 2016
PDF: 9 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570D (4 May 2016); doi: 10.1117/12.2225157
Show Author Affiliations
Jun Fang, Univ. of Electronic Science and Technology of China (China)
Lizao Zhang, Univ. of Electronic Science and Technology of China (China)
Huiping Duan, Univ. of Electronic Science and Technology of China (China)
Lei Huang, Shenzhen Univ. (China)
Hongbin Li, Stevens Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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