A Multi-Baseline Phase Unwrapping Algorithm Based on Integrated Processing of Intercept Pre-Filtering and Ambiguity Number Vector Determination.
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| Title: | A Multi-Baseline Phase Unwrapping Algorithm Based on Integrated Processing of Intercept Pre-Filtering and Ambiguity Number Vector Determination. |
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| Authors: | Wang, Zhen1 (AUTHOR), Xing, Chao1,2 (AUTHOR), Li, Xuemao1 (AUTHOR), Liu, Peng1,2 (AUTHOR) 23021110333@stu.xidian.edu.cn, Huang, Long2 (AUTHOR), Zhou, Chaowei2 (AUTHOR), Li, Zhenfang1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1340. 26p. |
| Subjects: | Phase unwrapping (Digital image processing), Radar interferometry, Optimization algorithms, Cluster analysis (Statistics), Signal processing |
| Abstract: | Highlights: What are the main findings? Extends the ambiguity number linear model and establishes a strict one-to-one mapping between the reference intercepts and ambiguity number combinations. Achieves integrated processing of intercept pre-filtering and ambiguity number vector determination. What are the implications of the main finding? Resolves cluster group loss, cluster centerline offset and low computational efficiency of traditional cluster analysis-based algorithms. Improves noise robustness and system adaptability for practical InSAR data processing in complex scenes. Multi-baseline phase unwrapping is a critical procedure in interferometric synthetic aperture radar (InSAR) data processing, and cluster analysis (CA)-based algorithms have become an important research direction in this field. However, traditional CA algorithms suffer from cluster group loss, cluster centerline offset under high noise, and time-consuming search, leading to limited unwrapping performance. To address these issues, this article proposes a multi-baseline phase unwrapping algorithm based on the integrated processing of intercept pre-filtering and ambiguity number vector determination, achieving significant performance improvements through four core technical optimisations. First, the linear relationship model of ambiguity numbers is extended to be compatible not only with the traditional one-transmitter, multi-receiver architecture but also with distributed multi-baseline InSAR systems with independent transmit–receive links for each baseline. Second, through verification from both forward and reverse uniqueness perspectives, a strict one-to-one mapping relationship between reference intercepts and ambiguity number combinations is established and validated. Third, a double constraints screening strategy for ambiguity number combinations combining the single-baseline elevation range intersection constraint and the multi-baseline elevation space common intersection constraint is designed. Integrating the effective elevation range of the observation area, this strategy accurately filters out valid ambiguity number combinations with physical rationality, ensuring the reliability of the reference intercept vector. Fourth, an intercept pre-filtering method based on the reference intercept vector is proposed, which unifies actual intercept pre-filtering and ambiguity number vector determination. To verify the performance of the proposed algorithm, a simulation data experiment under varying noise levels and real data experiments are conducted. Results demonstrate that the algorithm can maintain intact cluster structures under complex noise conditions. It achieves a synergistic improvement in unwrapping accuracy and computational efficiency, and thus significantly outperforms comparative algorithms. The proposed algorithm achieves high precision and efficiency for multi-baseline InSAR processing in complex scenarios, with important application value in practical engineering. [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: 193715371 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multi-Baseline Phase Unwrapping Algorithm Based on Integrated Processing of Intercept Pre-Filtering and Ambiguity Number Vector Determination. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhen%22">Wang, Zhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Chao%22">Xing, Chao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xuemao%22">Li, Xuemao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Peng%22">Liu, Peng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 23021110333@stu.xidian.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Long%22">Huang, Long</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Chaowei%22">Zhou, Chaowei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhenfang%22">Li, Zhenfang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1340. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Phase+unwrapping+%28Digital+image+processing%29%22">Phase unwrapping (Digital image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Extends the ambiguity number linear model and establishes a strict one-to-one mapping between the reference intercepts and ambiguity number combinations. Achieves integrated processing of intercept pre-filtering and ambiguity number vector determination. What are the implications of the main finding? Resolves cluster group loss, cluster centerline offset and low computational efficiency of traditional cluster analysis-based algorithms. Improves noise robustness and system adaptability for practical InSAR data processing in complex scenes. Multi-baseline phase unwrapping is a critical procedure in interferometric synthetic aperture radar (InSAR) data processing, and cluster analysis (CA)-based algorithms have become an important research direction in this field. However, traditional CA algorithms suffer from cluster group loss, cluster centerline offset under high noise, and time-consuming search, leading to limited unwrapping performance. To address these issues, this article proposes a multi-baseline phase unwrapping algorithm based on the integrated processing of intercept pre-filtering and ambiguity number vector determination, achieving significant performance improvements through four core technical optimisations. First, the linear relationship model of ambiguity numbers is extended to be compatible not only with the traditional one-transmitter, multi-receiver architecture but also with distributed multi-baseline InSAR systems with independent transmit–receive links for each baseline. Second, through verification from both forward and reverse uniqueness perspectives, a strict one-to-one mapping relationship between reference intercepts and ambiguity number combinations is established and validated. Third, a double constraints screening strategy for ambiguity number combinations combining the single-baseline elevation range intersection constraint and the multi-baseline elevation space common intersection constraint is designed. Integrating the effective elevation range of the observation area, this strategy accurately filters out valid ambiguity number combinations with physical rationality, ensuring the reliability of the reference intercept vector. Fourth, an intercept pre-filtering method based on the reference intercept vector is proposed, which unifies actual intercept pre-filtering and ambiguity number vector determination. To verify the performance of the proposed algorithm, a simulation data experiment under varying noise levels and real data experiments are conducted. Results demonstrate that the algorithm can maintain intact cluster structures under complex noise conditions. It achieves a synergistic improvement in unwrapping accuracy and computational efficiency, and thus significantly outperforms comparative algorithms. The proposed algorithm achieves high precision and efficiency for multi-baseline InSAR processing in complex scenarios, with important application value in practical engineering. [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/rs18091340 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1340 Subjects: – SubjectFull: Phase unwrapping (Digital image processing) Type: general – SubjectFull: Radar interferometry Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Signal processing Type: general Titles: – TitleFull: A Multi-Baseline Phase Unwrapping Algorithm Based on Integrated Processing of Intercept Pre-Filtering and Ambiguity Number Vector Determination. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Zhen – PersonEntity: Name: NameFull: Xing, Chao – PersonEntity: Name: NameFull: Li, Xuemao – PersonEntity: Name: NameFull: Liu, Peng – PersonEntity: Name: NameFull: Huang, Long – PersonEntity: Name: NameFull: Zhou, Chaowei – PersonEntity: Name: NameFull: Li, Zhenfang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
