计算机科学
压缩传感
算法
合成孔径雷达
网格
稳健性(进化)
迭代重建
缩小
梯度下降
人工智能
数学
人工神经网络
生物化学
化学
几何学
基因
程序设计语言
作者
Mingfei Shao,Zhe Zhang,Jie Li,Jian Kang,Bingchen Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:62: 1-13
标识
DOI:10.1109/tgrs.2023.3345454
摘要
Sparse signal processing techniques, such as compressed sensing, are commonly employed in tomographic synthetic aperture radar (TomoSAR) imaging due to the sparsity present in the elevation direction. However, classical compressed sensing methods, such as ℓ 1 norm regularization and orthogonal matching pursuit (OMP), suffer from the off-grid effect. Specifically, they discretize the elevation axis into multiple grids and assume that scatterers are located precisely on the grids, leading to reconstruction results that deviate from the true heights of the scatterers. Although gridless compressed sensing methods, such as atomic norm minimization (ANM), have achieved gridless reconstruction in specific scenarios, they face challenges such as the requirement of uniformly distributed baselines and large computational cost. In this paper, we propose a gridless compressed sensing method based on the alternate descent conditional gradient (ADCG) kernel for TomoSAR inversion, and compare it with ANM, iterative soft thresholding (IST), and OMP. We show through numerical simulations and experimental results that our proposed method is applicable not only to scenarios with uniformly distributed baselines, but also to scenarios with non-uniformly distributed baselines, and resolves the off-grid effect in both cases. Finally, we demonstrate the effectiveness of our proposed method by implementing gridless reconstruction using an actual dataset of urban buildings in Yuncheng.
科研通智能强力驱动
Strongly Powered by AbleSci AI