逆合成孔径雷达
奇异值分解
计算机科学
汉克尔矩阵
低秩近似
算法
矩阵范数
稀疏矩阵
矩阵完成
合成孔径雷达
秩(图论)
奇异值
压缩传感
基质(化学分析)
雷达成像
反问题
矩阵分解
雷达
人工智能
数学
特征向量
物理
组合数学
数学分析
量子力学
复合材料
高斯分布
材料科学
电信
作者
Gang Xu,Bangjie Zhang,Jianlai Chen,Fan Wu,Jialian Sheng,Wei Hong
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2021-10-14
卷期号:60: 1-12
被引量:8
标识
DOI:10.1109/tgrs.2021.3118083
摘要
There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.
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