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http://dx.doi.org/10.3837/tiis.2019.02.010

Tucker Modeling based Kronecker Constrained Block Sparse Algorithm  

Zhang, Tingping (School of Information Science and Engineering, Chongqing Jiaotong University)
Fan, Shangang (College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications)
Li, Yunyi (College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications)
Gui, Guan (College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications)
Ji, Yimu (School of Computer Science, Nanjing University of Posts and Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.2, 2019 , pp. 657-667 More about this Journal
Abstract
This paper studies synthetic aperture radar (SAR) imaging problem which the scatterers are often distributed in block sparse pattern. To exploiting the sparse geometrical feature, a Kronecker constrained SAR imaging algorithm is proposed by combining the block sparse characteristics with the multiway sparse reconstruction framework with Tucker modeling. We validate the proposed algorithm via real data and it shows that the our algorithm can achieve better accuracy and convergence than the reference methods even in the demanding environment. Meanwhile, the complexity is smaller than that of the existing methods. The simulation experiments confirmed the effectiveness of the algorithm as well.
Keywords
SAR imaging; Tucker decomposition; compressed sensing; Kronecker structure; multiway block sparsity;
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