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Proceedings Paper

Radar correlated imaging for extended target by the clustered sparse Bayesian learning with Laplace prior
Author(s): Tingting Qian; Guanghua Lu; Guochao Wang
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Paper Abstract

Radar correlated imaging (RCI) is a novel modality to obtain high resolution target images by correlated process of stochastic radiation field and the received signals. Conventional RCI methods neglect the inherent structure information of complex extended target, which makes the quality of recovery result degraded. Thus a clustered sparse Bayesian learning with Laplace prior (La-CSBL) algorithm for extended target imaging is proposed in this paper. A hierarchical correlated Laplace prior model is introduced to consider both the sparse prior and the cluster prior, and the prior for each coefficient not only involves its own hyperparameter, but also its immediate neighbor hyperparameters. Then the algorithm alternates between steps of target reconstruction and parameter optimization by cyclic minimization method under the Bayesian maximum a posteriori framework. Experimental results show that the proposed algorithm could realize high resolution imaging efficiently for extended target.

Paper Details

Date Published: 9 August 2018
PDF: 6 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108063L (9 August 2018); doi: 10.1117/12.2502961
Show Author Affiliations
Tingting Qian, Univ. of Science and Technology of China (China)
Guanghua Lu, Univ. of Science and Technology of China (China)
Guochao Wang, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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