韦顺军,,
田博坤,
张晓玲,
师君
电子科技大学信息与通信学院 ??成都 ??611731
基金项目:国家自然科学基金(61501098)、博士后面上基金(2015M570778)、高分青年基金(GFZX04061502)
详细信息
作者简介:田博坤(1993–)，男，河北沧州人，电子科技大学博士生，主要从事合成孔径雷达成像研究。E-mail: 3544348143@qq.com
通讯作者:韦顺军 ?weishunjun@uestc.edu.cn
中图分类号:TN957.5计量
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被引次数:0
出版历程
收稿日期:2018-08-31
修回日期:2018-12-15
LASAR High-resolution 3D Imaging Algorithm Based on Sparse Bayesian Regularization
Yan Min,Wei Shunjun,,
Tian Bokun,
Zhang Xiaoling,
Shi Jun
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Funds:The National Natural Science Foundation of China (61501098), The China Postdoctoral Science Foundation (2015M570778), the High Resolution Earth Observation Youth Foundation (GFZX04061502)
摘要
摘要:阵列合成孔径雷达(Linear Array Synthetic Aperture Radar, LASAR) 3维成像技术是一种具有重要潜在应用价值的雷达成像新体制，但受线阵天线及平台尺寸限制，传统匹配滤波成像算法难以实现LASAR高分辨3维成像。该文利用LASAR回波信号及观测目标的先验分布特性，提出了一种基于快速稀疏贝叶斯正则化重构的LASAR高分辨3维成像算法。该算法先结合贝叶斯估计准则及最大似然估计原理，构造LASAR目标重构的稀疏贝叶斯最小化代价函数；再利用迭代正则化方法求解联合范数最优化问题实现LASAR稀疏目标高分辨3维成像。另外，针对稀疏贝叶斯正则化成像运算量大的问题，结合位置预测快速成像思路，利用阈值分割算法对稀疏粗成像进行强目标提取，进而提升算法运算效率。仿真数据和实测数据验证了该文算法的有效性。
关键词:阵列合成孔径雷达/
3维成像/
压缩感知/
稀疏贝叶斯/
稀疏重构
Abstract:Linear Array Synthetic Aperture Radar (LASAR) is a novel and promising radar imaging technique. It is difficult to achieve high-resolution LASAR three-dimensional (3D) imaging using the traditional imaging methods based on match filter, because of limitations by the sizes of the linear array antenna and the platform. In this paper, by exploiting the prior distribution of the LASAR echoes and the observed scene, an LASAR high-resolution 3D algorithm based on sparse Bayesian regularization is proposed. The algorithm first combines the Bayesian principle and maximum likelihood estimation theory, and then a sparse Bayesian minimum cost function is constructed for LASAR target recovery. Second, using an iterative regularization reconstruction method, high-resolution imaging of LASAR sparse targets is achieved by solving a joint-norms optimization problem. In addition, for the problem of a large amount of sparse Bayesian regularization imaging, combined with the position prediction fast imaging idea, the threshold segmentation algorithm is used to extract the strong target of sparse coarse imaging, and then the algorithm operation efficiency is improved. Simulation and experiment results are presented to confirm the effectiveness of the algorithm.
Key words:Linear Array Synthetic Aperture Radar (LASAR)/
Three-dimensional imaging/
Compressed Sensing (CS)/
Sparse Bayesian/
Sparse recovery
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