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An Adaptive Sequential Phase Optimization Method Based on Coherence Stability Detection and Adjustment Correction.

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Title: An Adaptive Sequential Phase Optimization Method Based on Coherence Stability Detection and Adjustment Correction.
Authors: Li, Shijin1 (AUTHOR), Gao, Yandong1,2 (AUTHOR) ydgao@cumt.edu.cn, Zheng, Nanshan1,3 (AUTHOR), Bian, Hefang1 (AUTHOR), Mao, Yachun2 (AUTHOR), Duan, Wei1,3 (AUTHOR), Yuan, Yafei1 (AUTHOR), Chen, Qiang3 (AUTHOR), Ji, Binhe1 (AUTHOR)
Source: Remote Sensing. Dec2025, Vol. 17 Issue 23, p3818. 24p.
Subjects: Adaptive signal processing, Deformations (Mechanics), Phase distortion (Electronics), Signal processing, Calibration
Abstract: Highlights: Establish the submatrix dimension adaptive estimation model driven by coherence stability detection. Introduce the submatrix overlap criterion for covariance matrix adaptive sequential partitioning. Propose a phase reference adjustment correction method based on a weighted least squares estimator. Phase optimization, aimed to enhance phase signal-to-noise ratio, is a critical component of the distributed scatterer interferometric synthetic aperture radar technique and directly determines the fineness and reliability of deformation monitoring. As a state-of-the-art method that balances computational efficiency and optimization performance in high-dimensional data environments, sequential phase optimization has been widely studied. However, the improper matrix partitioning and discontinuous sequence compensation in current sequential methods severely restrict their optimization performance. To address these limitations, an adaptive sequential phase optimization method (AdSeq) based on coherence stability detection and adjustment correction is proposed. A submatrix dimension adaptive estimation model driven by coherence stability detection is first established based on persistent exceedance detection analysis. Then, a covariance matrix adaptive sequential partitioning strategy is developed by introducing the submatrix overlap criterion. Finally, a phase reference correction model based on weighted least squares adjustment is proposed to improve phase continuity and overall optimization performance. Experiments with simulated and real datasets are performed to comprehensively evaluate the optimization performance. Experimental results demonstrate that, compared with traditional phase optimization methods, the monitoring point density obtained by AdSeq increased by over 21.07%, and the deformation monitoring accuracy reached 16.49 mm, representing an improvement exceeding 10.09%. These results confirm that the proposed AdSeq method achieves superior noise robustness and phase optimization performance, and provides a higher deformation monitoring accuracy. [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.)
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  Data: An Adaptive Sequential Phase Optimization Method Based on Coherence Stability Detection and Adjustment Correction.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Dec2025, Vol. 17 Issue 23, p3818. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+signal+processing%22">Adaptive signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Deformations+%28Mechanics%29%22">Deformations (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+distortion+%28Electronics%29%22">Phase distortion (Electronics)</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink>
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  Data: Highlights: Establish the submatrix dimension adaptive estimation model driven by coherence stability detection. Introduce the submatrix overlap criterion for covariance matrix adaptive sequential partitioning. Propose a phase reference adjustment correction method based on a weighted least squares estimator. Phase optimization, aimed to enhance phase signal-to-noise ratio, is a critical component of the distributed scatterer interferometric synthetic aperture radar technique and directly determines the fineness and reliability of deformation monitoring. As a state-of-the-art method that balances computational efficiency and optimization performance in high-dimensional data environments, sequential phase optimization has been widely studied. However, the improper matrix partitioning and discontinuous sequence compensation in current sequential methods severely restrict their optimization performance. To address these limitations, an adaptive sequential phase optimization method (AdSeq) based on coherence stability detection and adjustment correction is proposed. A submatrix dimension adaptive estimation model driven by coherence stability detection is first established based on persistent exceedance detection analysis. Then, a covariance matrix adaptive sequential partitioning strategy is developed by introducing the submatrix overlap criterion. Finally, a phase reference correction model based on weighted least squares adjustment is proposed to improve phase continuity and overall optimization performance. Experiments with simulated and real datasets are performed to comprehensively evaluate the optimization performance. Experimental results demonstrate that, compared with traditional phase optimization methods, the monitoring point density obtained by AdSeq increased by over 21.07%, and the deformation monitoring accuracy reached 16.49 mm, representing an improvement exceeding 10.09%. These results confirm that the proposed AdSeq method achieves superior noise robustness and phase optimization performance, and provides a higher deformation monitoring accuracy. [ABSTRACT FROM AUTHOR]
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  Label:
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  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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        Text: English
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        PageCount: 24
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        Type: general
      – SubjectFull: Deformations (Mechanics)
        Type: general
      – SubjectFull: Phase distortion (Electronics)
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      – SubjectFull: Signal processing
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      – SubjectFull: Calibration
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              M: 12
              Text: Dec2025
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              Y: 2025
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