Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing
Abstract Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, sup...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-05-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12092 |
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| _version_ | 1832546813698113536 |
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| author | Lv Mingjiu Ma Lei Ma Jianchao Chen Wenfeng Yang Jun Ma Xiaoyan Cheng Qi |
| author_facet | Lv Mingjiu Ma Lei Ma Jianchao Chen Wenfeng Yang Jun Ma Xiaoyan Cheng Qi |
| author_sort | Lv Mingjiu |
| collection | DOAJ |
| description | Abstract Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods. |
| format | Article |
| id | doaj-art-7b3dbac9302f486794d82de08faa4fce |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-7b3dbac9302f486794d82de08faa4fce2025-02-03T06:47:11ZengWileyIET Signal Processing1751-96751751-96832022-05-0116331032610.1049/sil2.12092Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensingLv Mingjiu0Ma Lei1Ma Jianchao2Chen Wenfeng3Yang Jun4Ma Xiaoyan5Cheng Qi6Radar NCO School Air Force Early Warning Academy Wuhan ChinaShanghai Electro‐Mechanical Engineering Institute Shanghai ChinaRadar NCO School Air Force Early Warning Academy Wuhan ChinaEarly Warning Technology Air Forces Early Warning Academy Wuhan ChinaEarly Warning Technology Air Forces Early Warning Academy Wuhan ChinaEarly Warning Technology Air Forces Early Warning Academy Wuhan ChinaRadar NCO School Air Force Early Warning Academy Wuhan ChinaAbstract Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.https://doi.org/10.1049/sil2.12092radar imagingradar resolutionradar signal processing |
| spellingShingle | Lv Mingjiu Ma Lei Ma Jianchao Chen Wenfeng Yang Jun Ma Xiaoyan Cheng Qi Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing IET Signal Processing radar imaging radar resolution radar signal processing |
| title | Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing |
| title_full | Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing |
| title_fullStr | Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing |
| title_full_unstemmed | Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing |
| title_short | Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing |
| title_sort | fast super resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing |
| topic | radar imaging radar resolution radar signal processing |
| url | https://doi.org/10.1049/sil2.12092 |
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