Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm.
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| Title: | Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm. |
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| Authors: | Liu, Ke1 (AUTHOR) liuke18@nudt.edu.cn, Li, Yueli1 (AUTHOR) liyueli4uwb@nudt.edu.cn, Xu, Zhou2,3 (AUTHOR) xuzhou17hf@nudt.edu.cn, Zhou, Zhuojie1 (AUTHOR) tianjin@nudt.edu.cn, Jin, Tian1 (AUTHOR) |
| Source: | Remote Sensing. Nov2024, Vol. 16 Issue 22, p4121. 18p. |
| Subjects: | High resolution imaging, Magnitude estimation, Autonomous vehicles, Beamforming, Problem solving, Multichannel communication |
| Abstract: | Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 181203352 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Ke%22">Liu, Ke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuke18@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Yueli%22">Li, Yueli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liyueli4uwb@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhou%22">Xu, Zhou</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> xuzhou17hf@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zhuojie%22">Zhou, Zhuojie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tianjin@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jin%2C+Tian%22">Jin, Tian</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Nov2024, Vol. 16 Issue 22, p4121. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Magnitude+estimation%22">Magnitude estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Beamforming%22">Beamforming</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Multichannel+communication%22">Multichannel communication</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs16224121 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 4121 Subjects: – SubjectFull: High resolution imaging Type: general – SubjectFull: Magnitude estimation Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Beamforming Type: general – SubjectFull: Problem solving Type: general – SubjectFull: Multichannel communication Type: general Titles: – TitleFull: Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Ke – PersonEntity: Name: NameFull: Li, Yueli – PersonEntity: Name: NameFull: Xu, Zhou – PersonEntity: Name: NameFull: Zhou, Zhuojie – PersonEntity: Name: NameFull: Jin, Tian IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 16 – Type: issue Value: 22 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
