High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization
In practical combat scenarios, the high-resolution imaging process of synthetic aperture radar (SAR) is susceptible to diverse forms of electromagnetic jamming, which severely degrades the final imaging quality. To achieve high-fidelity SAR imaging under electronic countermeasure environments, this...
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| Format: | Article |
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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/11112573/ |
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| author | Xinrui Li Baixiao Chen Jingtian Xu |
| author_facet | Xinrui Li Baixiao Chen Jingtian Xu |
| author_sort | Xinrui Li |
| collection | DOAJ |
| description | In practical combat scenarios, the high-resolution imaging process of synthetic aperture radar (SAR) is susceptible to diverse forms of electromagnetic jamming, which severely degrades the final imaging quality. To achieve high-fidelity SAR imaging under electronic countermeasure environments, this article proposes a collaborative sparse signal optimization framework to reconstruct the SAR signals. By exploiting the sparsity of SAR signals and composite jamming signals in distinct domains, dictionaries are constructed separately for each, thereby establishing a collaborative sparse optimization problem. Furthermore, an inertial symmetric regularized alternating direction method of multipliers (ISRADMM) is developed to solve the formulated optimization problem through iterative computation. The proposed method achieves efficient and high-fidelity SAR imaging in composite jamming scenarios, thereby significantly enhancing subsequent target discrimination capability, as validated by entropy-based metrics. Comprehensive validation through numerical studies, simulations, and measured data experiments demonstrates the superior signal recovery and high-fidelity SAR imaging capability of this methodology under composite jamming scenarios. |
| format | Article |
| id | doaj-art-39fc8337d891429a914f5d55d4f1f41e |
| institution | Kabale University |
| 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-39fc8337d891429a914f5d55d4f1f41e2025-08-25T23:00:16ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118205132052910.1109/JSTARS.2025.359555811112573High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal OptimizationXinrui Li0https://orcid.org/0009-0003-5103-7276Baixiao Chen1https://orcid.org/0000-0001-7320-327XJingtian Xu2https://orcid.org/0009-0001-4116-6694National 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, ChinaIn practical combat scenarios, the high-resolution imaging process of synthetic aperture radar (SAR) is susceptible to diverse forms of electromagnetic jamming, which severely degrades the final imaging quality. To achieve high-fidelity SAR imaging under electronic countermeasure environments, this article proposes a collaborative sparse signal optimization framework to reconstruct the SAR signals. By exploiting the sparsity of SAR signals and composite jamming signals in distinct domains, dictionaries are constructed separately for each, thereby establishing a collaborative sparse optimization problem. Furthermore, an inertial symmetric regularized alternating direction method of multipliers (ISRADMM) is developed to solve the formulated optimization problem through iterative computation. The proposed method achieves efficient and high-fidelity SAR imaging in composite jamming scenarios, thereby significantly enhancing subsequent target discrimination capability, as validated by entropy-based metrics. Comprehensive validation through numerical studies, simulations, and measured data experiments demonstrates the superior signal recovery and high-fidelity SAR imaging capability of this methodology under composite jamming scenarios.https://ieeexplore.ieee.org/document/11112573/Composite electromagnetic jammingsignal recoverysingle-channelsynthetic aperture radar (SAR) |
| spellingShingle | Xinrui Li Baixiao Chen Jingtian Xu High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Composite electromagnetic jamming signal recovery single-channel synthetic aperture radar (SAR) |
| title | High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization |
| title_full | High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization |
| title_fullStr | High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization |
| title_full_unstemmed | High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization |
| title_short | High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization |
| title_sort | high fidelity sar imaging under composite jamming via collaborative sparse signal optimization |
| topic | Composite electromagnetic jamming signal recovery single-channel synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/11112573/ |
| work_keys_str_mv | AT xinruili highfidelitysarimagingundercompositejammingviacollaborativesparsesignaloptimization AT baixiaochen highfidelitysarimagingundercompositejammingviacollaborativesparsesignaloptimization AT jingtianxu highfidelitysarimagingundercompositejammingviacollaborativesparsesignaloptimization |