DLSLA 3-D SAR Imaging via Sparse Recovery Through Combination of Nuclear Norm and Low-Rank Matrix Factorization

Downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track dimensional imaging always suffers from incomplete observation which does not satisfy the Nyquist sampling theorem and leads to the failure of conventional 3-D frequency-domain methods. Although several spa...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 13
Hlavní autoři: Gu, Tong, Liao, Guisheng, Li, Yachao, Guo, Yifan, Liu, Yongjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Beamforming
Comparative analysis
Comparative studies
Computer simulation
Data recovery
Doppler sonar
Error compensation
Factorization
Geometry
Image degradation
Image processing
Image reconstruction
Imaging
Imaging techniques
Iterative methods
Linear arrays
Low-rank matrix factorization (LRMF)
Mathematical analysis
Mathematical models
Matrices (mathematics)
matrix completion (MC)
nuclear norm
Radar arrays
Radar imaging
Radar polarimetry
Recovery
Robustness (mathematics)
Sampled data
SAR (radar)
Ship models
Sparse matrices
sparse recovery
Synthetic aperture radar
Three dimensional imaging
Three dimensional models
vector reconstruction framework
ISSN:0196-2892, 1558-0644
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Shrnutí:Downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track dimensional imaging always suffers from incomplete observation which does not satisfy the Nyquist sampling theorem and leads to the failure of conventional 3-D frequency-domain methods. Although several sparse reconstruction-based methods have been presented to solve this problem, the basis mismatch issue in sparse reconstruction theory will degrade the image reconstruction performance. To address this issue, this article proposes a novel 3-D imaging method for DLSLA 3-D SAR, which provides another idea for 3-D imaging through sparse recovery. It utilizes recovered full-sampled data to achieve cross-track dimensional imaging instead of using the under-sampled data directly as before. The Along-track-Height plane imaging is first finished by the range-Doppler (RD) algorithm and motion error compensation. Then, an advanced nuclear norm and low-rank matrix factorization (NU-LRMF)-based matrix completion (MC) algorithm and a vector reconstruction framework are built to achieve accurate recovery of full-sampled data. Finally, the cross-track dimensional imaging is completed with recovered full-sampled data by geometric correction and beamforming. Moreover, a fast two-stage iteration strategy for NU-LRMF (TS-NU-LRMF) is also presented to accelerate convergence. The robustness and effectiveness of the proposed 3-D imaging method are verified by several numerical simulations and comparative studies based on both the complex 3-D ship model and the simulated 3-D distributed scenario.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3100715