Microwave Imaging by Multitask Bayesian Compressed Sensing Within Contrast Source Framework

被引：0
作者：
Zhang Q.-H. [1 ]
Yu S.-Q. [1 ]
Shi L.-P. [1 ]
Zhang S.-H. [1 ]
机构：
[1] School of Computer and Information, China Three Gorges University, Yichang
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 11期
关键词：
Conjugate gradient; Contrast source; First order Born approximation; Laplacian priori; Microwave imaging; Multi-task Bayesian compressed sensing;
D O I：
10.3969/j.issn.0372-2112.2020.11.016
中图分类号：
学科分类号：
摘要：
Aiming at the difficulty of microwave imaging of strong scatterers, a multi-task Bayesian compressed sensing method based on Laplacian priori is proposed, which realizes microwave imaging of sparse strong scatterers.In the framework of contrast sources, sparse sensing model is established based on the "data" integral equation and the mesh discretization in the imaging region.The forward problem is simulated by the moment method; a Bayesian compressed sensing hierarchical model based on Laplacian priori is constructed; and in the case of multi-incident waves, multi-task Bayesian compressed sensing method is used to optimize the contrast source.Finally, the objective function is reconstructed by using the "state equation".Considering the influence of noise, Through the numerical simulation of multi-pixel single target, non-uniform single target and multi-target microwave imaging, and compared with the reconstructed results of conjugate gradient method and multi-task Bayesian compressed sensing method in the first-order Born approximation framework, which verifies the effectiveness and robustness of the proposed algorithm. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：2208 / 2214
页数：6
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