张双辉,,
张弛,
刘永祥
国防科技大学电子科学学院 长沙 410073
基金项目:国家自然科学基金(61801484, 61921001)
详细信息
作者简介:邓理康(1991–)，男，福建建阳人，国防科技大学电子科学学院在读研究生，研究方向为双站雷达成像、MIMO雷达成像
张双辉(1989–)，男，湖南长沙人，博士，国防科技大学电子科学学院副研究员，研究方向为雷达成像、压缩感知、贝叶斯推断
张弛：张 弛(1994–)，男，湖北孝感人，国防科技大学电子科学学院在读博士生，研究方向为雷达成像、压缩感知、贝叶斯学习
刘永祥(1976–)，男，河北唐山人，博士，国防科技大学电子科学学院教授，博士生导师，研究方向为目标微动特性分析与识别
通讯作者:张双辉 shzhang3@126.com
责任主编：张弓 Corresponding Editor: ZHANG Gong中图分类号:TN957.51
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被引次数:0
出版历程
收稿日期:2020-10-19
修回日期:2021-01-27
网络出版日期:2021-02-08
刊出日期:2021-06-28
A Multiple-Input Multiple-Output Inverse Synthetic Aperture Radar Imaging Method Based on Multidimensional Alternating Direction Method of Multipliers
DENG Likang,ZHANG Shuanghui,,
ZHANG Chi,
LIU Yongxiang
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Funds:The National Natural Science Foundation of China (61801484, 61921001)
More Information
Corresponding author:ZHANG Shuanghui, shzhang3@126.com
摘要
摘要:基于傅里叶变换的传统逆合成孔径雷达(ISAR)成像方法存在数据存储量大、数据采集时间长的问题。压缩感知(CS)理论利用图像的稀疏性，可以利用有限的数据恢复图像，这极大降低了数据采集成本。但对于多维数据，传统压缩感知方法要将多维数据转化成一维向量，这造成了很大存储和计算负担。因此，该文提出一种基于多维度-交替方向乘子法(MD-ADMM)的多输入多输出-逆合成孔径雷达(MIMO-ISAR)成像快速稀疏重建方法。首先建立基于张量信号的压缩感知模型，然后用ADMM算法对模型进行优化，将测量矩阵分解为张量模态积，用张量元素除法替代矩阵求逆，显著减少所需的内存和计算负担。该方法只需少量的数据采样，就能实现快速成像。与其他基于张量的压缩感知方法相比，该方法具有鲁棒性强、图像质量好、计算效率高的优点。仿真和实测数据验证了该方法的有效性。
关键词:多维度-交替方向乘子法/
压缩感知/
多输入多输出-逆合成孔径雷达
Abstract:The disadvantages of the traditional Inverse Synthetic Aperture Radar (ISAR) imaging method based on Fourier transform include large data storage and long collection time. The Compressive Sensing (CS) theory can use limited data to restore an image with the sparsity of the image, reducing the cost of data collection. However for multidimensional data, the traditional compressive sensing methods need to convert three-dimensional data into a one-dimensional vector, causing the storage and calculation burden. Therefore, this study proposes a fast MultiDimensional Alternating Direction Method of Multipliers ((MD-ADMM)) sparse reconstruction method for Multiple-Input Multiple-Output ISAR (MIMO-ISAR) imaging. The CS model based on the tensor signal was established, and the model with the ADMM algorithm was optimized. The measured matrix is decomposed into a tensor modal product, and matrix inversion is replaced by tensor element division, significantly reducing memory consumption and computational burden. Fast ISAR imaging can be achieved by a small amount of data sampling by the proposed method. Compared with other tensor compressed sensing methods, this method has the advantages of stronger robustness, higher image quality, and computational efficiency. The effectiveness of the proposed method can be invalidated by simulated and measured data.
Key words:MultiDimensional Alternating Direction Method of Multipliers (MD-ADMM)/
Compressive Sensing (CS)/
Multiple-Input Multiple-Output Inverse Synthetic Aperture Radar (MIMO-ISAR)
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