Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints
Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/3/609 |
| _version_ | 1827759589200756736 |
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| author | Junkui Tang Zheng Liu Lei Ran Rong Xie Jikai Qin |
| author_facet | Junkui Tang Zheng Liu Lei Ran Rong Xie Jikai Qin |
| author_sort | Junkui Tang |
| collection | DOAJ |
| description | Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods. |
| first_indexed | 2024-03-11T09:27:48Z |
| format | Article |
| id | doaj.art-db6758e11e8b4bfea671f41a1eb47525 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:27:48Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-db6758e11e8b4bfea671f41a1eb475252023-11-16T17:51:41ZengMDPI AGRemote Sensing2072-42922023-01-0115360910.3390/rs15030609Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank ConstraintsJunkui Tang0Zheng Liu1Lei Ran2Rong Xie3Jikai Qin4National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaMultiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods.https://www.mdpi.com/2072-4292/15/3/609multiple-input multiple-output (MIMO)forward-looking imagingcompressed sensing (CS)sparse and double low-rank constraintsaugmented Lagrangian multiplier (ALM)alternating direction multiplier method (ADMM) |
| spellingShingle | Junkui Tang Zheng Liu Lei Ran Rong Xie Jikai Qin Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints Remote Sensing multiple-input multiple-output (MIMO) forward-looking imaging compressed sensing (CS) sparse and double low-rank constraints augmented Lagrangian multiplier (ALM) alternating direction multiplier method (ADMM) |
| title | Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints |
| title_full | Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints |
| title_fullStr | Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints |
| title_full_unstemmed | Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints |
| title_short | Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints |
| title_sort | forward looking super resolution imaging of mimo radar via sparse and double low rank constraints |
| topic | multiple-input multiple-output (MIMO) forward-looking imaging compressed sensing (CS) sparse and double low-rank constraints augmented Lagrangian multiplier (ALM) alternating direction multiplier method (ADMM) |
| url | https://www.mdpi.com/2072-4292/15/3/609 |
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