A Fast Gridless Polarimetric HRRP Imaging Method Using Virtual Full Polarization.
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| Title: | A Fast Gridless Polarimetric HRRP Imaging Method Using Virtual Full Polarization. |
|---|---|
| Authors: | Li, Yingjun1 (AUTHOR), Zhang, Wenpeng1 (AUTHOR) zhangwenpeng@hotmail.com, Yang, Wei1 (AUTHOR), Zhang, Shuanghui1 (AUTHOR), Fu, Yaowen1 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1225. 28p. |
| Subjects: | Polarimetric remote sensing, Compressed sensing, Remote sensing, Optimization algorithms, Optical polarization |
| Abstract: | Highlights: What are the main findings? A novel virtual full-polarization sparse stepped-frequency waveform (VFP-SSFW) is proposed to eliminate transmission channel crosstalk for fully polarimetric measurements. A polarimetric atomic norm minimization (P-ANM)-based gridless imaging framework is formulated to produce high-quality polarimetric high-resolution range profiles (HRRPs), and an efficient alternating direction method of multipliers (ADMM)-based solver is tailored for its implementation. What are the implications of the main findings? The proposed VFP-SSFW theoretically eliminates transmission channel crosstalk, and offers the by-products of halved instantaneous transmission power and reduced sampling echoes. These features have promising potential, particularly for energy-limited orbital remote sensing platforms. The method efficiently produces refined, interpretable polarimetric HRRPs represented by scattering centers (SCs) co-registered across all channels, without requiring inter-channel SC matching, while maintaining robustness under low sampling rates (SRs) and low signal-to-noise ratios (SNRs). Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs. The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. [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: 193435704 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Fast Gridless Polarimetric HRRP Imaging Method Using Virtual Full Polarization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Yingjun%22">Li, Yingjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wenpeng%22">Zhang, Wenpeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangwenpeng@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Wei%22">Yang, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shuanghui%22">Zhang, Shuanghui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Yaowen%22">Fu, Yaowen</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 8, p1225. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polarimetric+remote+sensing%22">Polarimetric remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+polarization%22">Optical polarization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A novel virtual full-polarization sparse stepped-frequency waveform (VFP-SSFW) is proposed to eliminate transmission channel crosstalk for fully polarimetric measurements. A polarimetric atomic norm minimization (P-ANM)-based gridless imaging framework is formulated to produce high-quality polarimetric high-resolution range profiles (HRRPs), and an efficient alternating direction method of multipliers (ADMM)-based solver is tailored for its implementation. What are the implications of the main findings? The proposed VFP-SSFW theoretically eliminates transmission channel crosstalk, and offers the by-products of halved instantaneous transmission power and reduced sampling echoes. These features have promising potential, particularly for energy-limited orbital remote sensing platforms. The method efficiently produces refined, interpretable polarimetric HRRPs represented by scattering centers (SCs) co-registered across all channels, without requiring inter-channel SC matching, while maintaining robustness under low sampling rates (SRs) and low signal-to-noise ratios (SNRs). Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs. The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. [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/rs18081225 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1225 Subjects: – SubjectFull: Polarimetric remote sensing Type: general – SubjectFull: Compressed sensing Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Optical polarization Type: general Titles: – TitleFull: A Fast Gridless Polarimetric HRRP Imaging Method Using Virtual Full Polarization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Yingjun – PersonEntity: Name: NameFull: Zhang, Wenpeng – PersonEntity: Name: NameFull: Yang, Wei – PersonEntity: Name: NameFull: Zhang, Shuanghui – PersonEntity: Name: NameFull: Fu, Yaowen IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 8 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
