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Sparse Target Batch-Processing Framework for Scanning Radar Superresolution Imaging
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DOI：10.1109/LGRS.2023.3274910.png)
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摘要
En 中文
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 2-D 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 the batch-processing potential for 2-D 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 more suitable for 2-D echo data superresolution.
Keyword:
Superresolution
Azimuth
Sparse matrices
Radar imaging
Radar
Optimization
Convolution
Alternating direction method of multipliers (ADMM)
batch-processing
scanning radar
sparse superresolution
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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