Sparse Target Batch-processing Framework for Scanning Radar Superresolution Imaging

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for two-dimensional echo data. In this lette...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 20; p. 1
Main Authors: Tuo, Xingyu, Mao, Deqing, Zhang, Yin, Zhang, Yongchao, Huang, Yulin, Yang, Jianyu
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
ADMM
Algorithms
Azimuth
batch-processing
Convolution
Echoes
Frameworks
Imaging techniques
Iterative methods
Optimization
Radar
Radar imaging
Scanning
scanning radar
Sparse matrices
Sparse superresolution
Superresolution
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for two-dimensional echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents batch-processing potential for two-dimensional echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is much suitable for two-dimensional echo data superresolution.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3274910