A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method
Radar super-resolution imaging methods with joint low-rank and sparse constraints have garnered increasing attention. However, in complex imaging scenarios, the low-rank property of the signal matrix is often not prominent, which limits the performance of directly applying low-rank constraints in su...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10994991/ |
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| author | Junkui Tang Lei Ran Zheng Liu Rong Xie Yan Liu Genquan Han |
| author_facet | Junkui Tang Lei Ran Zheng Liu Rong Xie Yan Liu Genquan Han |
| author_sort | Junkui Tang |
| collection | DOAJ |
| description | Radar super-resolution imaging methods with joint low-rank and sparse constraints have garnered increasing attention. However, in complex imaging scenarios, the low-rank property of the signal matrix is often not prominent, which limits the performance of directly applying low-rank constraints in super-resolution imaging. To address this issue, this article proposes a weighted low-rank and sparse constraint-based multichannel radar forward-looking super-resolution imaging method. First, the proposed method calculates weighting coefficients using the covariance matrix of radar echoes and applies weighted constraints to the signal matrix, thereby enhancing its low-rank property and significantly improving forward-looking super-resolution imaging performance. Then, in solving the optimization problem, the alternating direction method of multipliers (ADMM) is employed to decompose variables and reduce the complexity of the solution. To further enhance computational efficiency, the symmetry of the weighting matrix and the characteristics of the dictionary matrix in sparse imaging, specifically as a partial Fourier dictionary, are leveraged. A fast matrix inversion method based on eigenvalue decomposition is proposed to mitigate the computational complexity induced by super-resolution requirements. Finally, the effectiveness and superiority of the proposed method in complex scenarios are validated through comparative experiments on simulated and measured data. |
| format | Article |
| id | doaj-art-b67a1a72df1d4bc6b05dc2db9f076de3 |
| institution | Matheson Library |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b67a1a72df1d4bc6b05dc2db9f076de32025-07-02T00:05:09ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118129731298710.1109/JSTARS.2025.356878310994991A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging MethodJunkui Tang0https://orcid.org/0009-0001-6652-8102Lei Ran1https://orcid.org/0000-0003-0687-7738Zheng Liu2https://orcid.org/0000-0001-6526-059XRong Xie3Yan Liu4https://orcid.org/0000-0001-5583-0587Genquan Han5https://orcid.org/0000-0001-5140-4150National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, ChinaSchool of Microelectronics, Xidian University, Xi'an, ChinaSchool of Microelectronics, Xidian University, Xi'an, ChinaRadar super-resolution imaging methods with joint low-rank and sparse constraints have garnered increasing attention. However, in complex imaging scenarios, the low-rank property of the signal matrix is often not prominent, which limits the performance of directly applying low-rank constraints in super-resolution imaging. To address this issue, this article proposes a weighted low-rank and sparse constraint-based multichannel radar forward-looking super-resolution imaging method. First, the proposed method calculates weighting coefficients using the covariance matrix of radar echoes and applies weighted constraints to the signal matrix, thereby enhancing its low-rank property and significantly improving forward-looking super-resolution imaging performance. Then, in solving the optimization problem, the alternating direction method of multipliers (ADMM) is employed to decompose variables and reduce the complexity of the solution. To further enhance computational efficiency, the symmetry of the weighting matrix and the characteristics of the dictionary matrix in sparse imaging, specifically as a partial Fourier dictionary, are leveraged. A fast matrix inversion method based on eigenvalue decomposition is proposed to mitigate the computational complexity induced by super-resolution requirements. Finally, the effectiveness and superiority of the proposed method in complex scenarios are validated through comparative experiments on simulated and measured data.https://ieeexplore.ieee.org/document/10994991/Alternating direction multiplier method (ADMM)covariance matrixeigenvalue decompositionforward-looking imagingweighted low-rank and sparse (WLRS) |
| spellingShingle | Junkui Tang Lei Ran Zheng Liu Rong Xie Yan Liu Genquan Han A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Alternating direction multiplier method (ADMM) covariance matrix eigenvalue decomposition forward-looking imaging weighted low-rank and sparse (WLRS) |
| title | A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method |
| title_full | A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method |
| title_fullStr | A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method |
| title_full_unstemmed | A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method |
| title_short | A Weighted Low-Rank and Sparse Constraint-Based Multichannel Radar Forward-Looking Imaging Method |
| title_sort | weighted low rank and sparse constraint based multichannel radar forward looking imaging method |
| topic | Alternating direction multiplier method (ADMM) covariance matrix eigenvalue decomposition forward-looking imaging weighted low-rank and sparse (WLRS) |
| url | https://ieeexplore.ieee.org/document/10994991/ |
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