An Enhanced Sequential ISAR Image Scatterer Trajectory Association Method Utilizing Modified Label Gaussian Mixture Probability Hypothesis Density Filter.
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| Title: | An Enhanced Sequential ISAR Image Scatterer Trajectory Association Method Utilizing Modified Label Gaussian Mixture Probability Hypothesis Density Filter. |
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| Authors: | Liu, Lei1 (AUTHOR) liulei@xidian.edu.cn, Zhou, Zuobang2 (AUTHOR), Li, Cheng1 (AUTHOR), Zhou, Feng1 (AUTHOR) |
| Source: | Remote Sensing. Feb2025, Vol. 17 Issue 3, p354. 26p. |
| Subjects: | Inverse synthetic aperture radar, Random sets, Motion analysis, Label design, Geometry |
| Abstract: | In the context of 3D geometric reconstruction from sequential inverse synthetic aperture radar (ISAR) images, the accurate scatterer trajectory association is a critical step. Aiming at the above problem, an enhanced scatterer trajectory association method is proposed by designing a modified label Gaussian mixture probability hypothesis density (ML-GM-PHD) filtering algorithm. The algorithm commences by constructing a general motion model for scatterers across sequential ISAR images, followed by an in-depth analysis of their motion characteristics. Subsequently, the actual projected positions and measurements of the scattering centers of the observed target are treated as random finite sets, which allows us to reformulate the scatterer trajectory association into a maximum a posteriori (MAP) estimation problem. After that, a ML-GM-PHD filtering algorithm is proposed to realize the scatterer trajectory association. Furthermore, the proposed method is applied to ISAR images in both the forward and reverse directions, enabling the fusion of trajectories from opposing directions to bolster the completeness of the scatterer trajectories. Finally, the factorization method is performed on the scatterer trajectory matrix to implement the 3D geometry reconstruction of the scattering centers in the observed target. Experimental results based on random points and electromagnetic data verify the effectiveness and performance priority of the proposed algorithm. [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: 182983006 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Enhanced Sequential ISAR Image Scatterer Trajectory Association Method Utilizing Modified Label Gaussian Mixture Probability Hypothesis Density Filter. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Lei%22">Liu, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liulei@xidian.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zuobang%22">Zhou, Zuobang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Cheng%22">Li, Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Feng%22">Zhou, Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2025, Vol. 17 Issue 3, p354. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Inverse+synthetic+aperture+radar%22">Inverse synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Random+sets%22">Random sets</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+analysis%22">Motion analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Label+design%22">Label design</searchLink><br /><searchLink fieldCode="DE" term="%22Geometry%22">Geometry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the context of 3D geometric reconstruction from sequential inverse synthetic aperture radar (ISAR) images, the accurate scatterer trajectory association is a critical step. Aiming at the above problem, an enhanced scatterer trajectory association method is proposed by designing a modified label Gaussian mixture probability hypothesis density (ML-GM-PHD) filtering algorithm. The algorithm commences by constructing a general motion model for scatterers across sequential ISAR images, followed by an in-depth analysis of their motion characteristics. Subsequently, the actual projected positions and measurements of the scattering centers of the observed target are treated as random finite sets, which allows us to reformulate the scatterer trajectory association into a maximum a posteriori (MAP) estimation problem. After that, a ML-GM-PHD filtering algorithm is proposed to realize the scatterer trajectory association. Furthermore, the proposed method is applied to ISAR images in both the forward and reverse directions, enabling the fusion of trajectories from opposing directions to bolster the completeness of the scatterer trajectories. Finally, the factorization method is performed on the scatterer trajectory matrix to implement the 3D geometry reconstruction of the scattering centers in the observed target. Experimental results based on random points and electromagnetic data verify the effectiveness and performance priority of the proposed algorithm. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182983006 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17030354 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 354 Subjects: – SubjectFull: Inverse synthetic aperture radar Type: general – SubjectFull: Random sets Type: general – SubjectFull: Motion analysis Type: general – SubjectFull: Label design Type: general – SubjectFull: Geometry Type: general Titles: – TitleFull: An Enhanced Sequential ISAR Image Scatterer Trajectory Association Method Utilizing Modified Label Gaussian Mixture Probability Hypothesis Density Filter. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Lei – PersonEntity: Name: NameFull: Zhou, Zuobang – PersonEntity: Name: NameFull: Li, Cheng – PersonEntity: Name: NameFull: Zhou, Feng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 3 Titles: – TitleFull: Remote Sensing Type: main |
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