.png){kind=icon}
返回
The Efficient Norm Regularization Method Applying on the ISAR Image With Sparse Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2022.3218581.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Inverse synthetic aperture radar (ISAR) image data of a single target is sparse in the image domain. Based on this sparseness, we could obtain a high-precision image reconstruction by down sampling the imaging data and getting the sparse solution of the indeterminate equations. In this work, we have studied the sparse data processing theory based on the compressed sensing (CS) method. We focus on the sparse reconstruction of the ISAR image. The imaging data are sparsely sampled and restored through the norm regularization framework. We compare the reconstruction results on L-1 and L-1/2 regularization frameworks, respectively. Then, we concentrate on the relationship between the reconstruction results and parameter settings in the reconstruction framework. Besides, we study the ISAR image in different radar bands. The numerical results show that the L-1/2 regularization framework is better than the L-1 framework in recovery accuracy and computational efficiency.
Keyword:
Inverse synthetic aperture radar (ISAR) image
norm regularization
sparse data
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
The Effects of Tree Canopy Structure and Tree Coverage Ratios on Urban Air Temperature Based on ENVI-Met.png)
Forests
OAAI
ForestsIF0
OAAI分享收藏Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏Homogeneous catalytic reduction of nitric oxide by olefins in the presence of palladium(II) chloride
PREAI
分享收藏分享收藏
查看更多内容