Super-Resolution for MIMO Array SAR 3-D Imaging Based on Compressive Sensing and Deep Neural Network Article Swipe
Chunxiao Wu
,
Zenghui Zhang
,
Longyong Chen
,
Wenxian Yu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2020.3000760
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2020.3000760
Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can straightly obtain the 3-D imagery of the illuminated scene with the single-pass flight. Generally, the Rayleigh resolution of the elevation direction is unacceptable due to the length limitation of linear array. The super-resolution imaging algorithms within the compressive sensing (CS) framework have been extensively studied because of the essential spatial sparsity in the elevation direction. However, the super-resolution performance of the existing sparse reconstruction algorithms will deteriorate dramatically in the case of lower signal-to-noise ratio (SNR) level or a few antenna elements. To overcome this problem, a new super-resolution imaging structure based on CS and deep neural network (DNN) for MIMO array SAR is proposed in this article. In this new algorithm, the spatial filtering based on CS is first proposed to reserve the signals only impinging from the prespecified space subregions. Thereafter, a group of parallel end-to-end DNN regression models are designed for mapping the potential sparse recovery mathematical model and further locating the true scatterers in the elevation direction. Finally, extensive simulations and airborne MIMO array SAR experiments are investigated to validate that the proposed method can realize the state-of-the-art super-resolution imaging against other existing related methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2020.3000760
- https://ieeexplore.ieee.org/ielx7/4609443/8994817/09112264.pdf
- OA Status
- gold
- Cited By
- 34
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3035248706
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3035248706Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2020.3000760Digital Object Identifier
- Title
-
Super-Resolution for MIMO Array SAR 3-D Imaging Based on Compressive Sensing and Deep Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Chunxiao Wu, Zenghui Zhang, Longyong Chen, Wenxian YuList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2020.3000760Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/4609443/8994817/09112264.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/4609443/8994817/09112264.pdfDirect OA link when available
- Concepts
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Computer science, MIMO, Synthetic aperture radar, Compressed sensing, Image resolution, Elevation (ballistics), Artificial intelligence, Signal-to-noise ratio (imaging), Algorithm, Phased array, Computer vision, Antenna (radio), Beamforming, Telecommunications, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
34Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 5, 2023: 5, 2022: 8, 2021: 6Per-year citation counts (last 5 years)
- References (count)
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71Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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