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  • 晋良念,蒋佳琪. 交替迭代最小化稀疏穿墙成像快速算法[J]. 雷达科学与技术, 2019, 17(4): 371-378.    [点击复制]
  • JIN Liangnian,JIANG Jiaqi. A Fast Algorithm for Sparse Through-Wall Imaging with Alternating Iterative Minimization[J]. Radar Science and Technology, 2019, 17(4): 371-378.   [点击复制]
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交替迭代最小化稀疏穿墙成像快速算法
晋良念,蒋佳琪
0
(1.桂林电子科技大学信息与通信学院,广西桂林541004;2.广西无线宽带通信与信号处理重点实验室,广西桂林541004)
摘要:
针对墙后目标成像分辨率与成像速度不能同时有效满足的问题,提出一种参数交替迭代最小化框架的墙后隐藏目标稀疏成像快速算法。首先,该算法利用稀疏信号贝叶斯模型的最大后验估计准则得到包含参数与成像体散射系数矢量的目标函数,然后在优化最小化框架(MM)下求解出对应的最优化函数,最后利用目标函数对应的优化函数对成像体散射系数、噪声功率和超参数进行交替迭代求解。仿真和实验结果表明,该方法对墙后点目标以及扩展目标进行高质量成像,并且大大提高算法速度。
关键词:  穿墙稀疏成像  参数交替迭代  优化最小化  快速成像
DOI:DOI:10.3969/j.issn.1672-2337.2019.04.004
基金项目:国家自然科学基金(No.61461012);广西自然科学基金(No.2017GXNSFAA198050);广西无线宽带通信与信号处理重点实验室2016主任基金项目(No.GXKL06160106);桂林电子科技大学研究生教育创新计划资助项目
A Fast Algorithm for Sparse Through-Wall Imaging with Alternating Iterative Minimization
JIN Liangnian,JIANG Jiaqi
(1.School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;2.Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing,Guilin 541004,China)
Abstract:
The resolution and the speed of the back wall target imaging can not be satisfied effectively at the same time,so this paper proposes a fast algorithm for hidden target sparse imaging behind the wall with a parameter alternative iterative minimization framework. Firstly,the maximum posteriori estimation criterion of the sparse signal Bayesian model is used to obtain objective function including the parameters. The objective function of imaging volume scattering coefficient vector is then solved under the optimization minimization framework (MM) to obtain the corresponding optimization function. Finally,using the optimization function corresponding to the objective function,the imaging volume scattering coefficient vector,noise power,and hyperparameter are solved through alternating and iterating. Simulation and experimental results show that this method performs high-quality imaging of point targets and extended targets behind the wall,and greatly improves the algorithms speed.
Key words:  through-wall sparse imaging  parameter alternating  iterative minimization  majorization-minimization  fast imaging